Ai Engineer Jobs

Summary: Candidates need foundational Python skills, awareness or curiosity regarding modern GenAI libraries and agentic architectures, and some exposure to cloud platforms. Strong communication, a builder mindset comfortable with ambiguity, and a high degree of curiosity are essential for this consulting-focused role.

Skills: python, genai, agentic tools, pinecone, huggingface, langchain, langsmith, crewai, cloud platforms, communication, prototyping, experimentation, autonomy, client engagement, debugging, ai/ml

Summary: Candidates must possess 3 or more years of experience in software or ML engineering post-BS/MS, with hands-on experience shipping production AI/ML systems or AI-powered products. Strong skills in building multi-step ML systems, backend/API development, and demonstrating startup grit in fast-moving environments are required.

Skills: ai agent development, model development, orchestration, productionization, evaluation, platform engineering, backend development, frontend development, api development, data pipelines, system architecture, inference optimization, scalability, reliability engineering, ml systems shipping

Summary: Candidates must possess strong Python programming experience, proficiency with cloud infrastructure and serverless compute tools like Modal, and familiarity with relational databases such as PostgreSQL. Essential requirements also include experience integrating with major LLM APIs or frameworks, coupled with excellent problem-solving skills and an autonomous builder's mindset.

Skills: python, llm apis, cloud infrastructure, serverless compute, postgresql, openai, anthropic, xai, google, pydanticai, problem-solving, modal

Summary: YC-funded startup with $1M+ ARR offering founding equity, autonomy to shape core AI agent infrastructure, and direct founders access; strong growth trajectory and enterprise focus.

Skills: python, typescript, aws, kubernetes, postgresql, redis, sql, llm, openai, frontier, orchestration, dag, workflows

Summary: Join a small, mission-driven research startup with a culture of ownership and collaboration; this role blends deep science with production-readiness and product impact; visa sponsorship available; strong growth and learning opportunities.

Skills: python, pytorch, llms, ai_infrastructure

Summary: Candidates must possess deep expertise in AI-powered application development, including hands-on experience with OpenAI APIs, LangChain, RAG, and AI agent orchestration, alongside strong backend proficiency in Python and Node.js/Express. A minimum of 8-10+ years of professional software engineering experience and a proven track record delivering complex, scalable AI systems in enterprise environments are required.

Skills: python, next.js, typescript, node.js, rag pipelines, ai agent orchestration, llm, openai, azure openai, langchain, langgraph, azure devops, tdd, bdd, clean architecture, ci/cd

Summary: Candidates should possess a solid level of experience with JavaScript and TypeScript, particularly in building dynamic web applications, and be comfortable working with React alongside backend systems. Successful candidates must be proactive problem-solvers who thrive in fast-paced environments, deliver high-quality products, and are eager to mentor others.

Skills: javascript, typescript, react, frontend, backend, real-time infrastructure, ai, debugging, optimization, testing, observability, ux

Summary: Candidates must possess 5+ years of experience with production-grade Python and solid software design, alongside deep expertise in LLM applications such as agent architectures and advanced RAG strategies. A product mindset comfortable with ambiguity is also required.

Skills: python, llm applications, agent architectures, rag strategies, context management, tool use, guardrails, sub-agent orchestration, observability, typescript, multimodal inputs, software design, evaluation frameworks, product mindset, deep learning, classical ml

Summary: Candidates need a Bachelor's or Master's degree in a technical field or equivalent experience, coupled with 3-5 years of experience deploying complex software or ML/AI models to production at scale. A solid grasp of Python, algorithms, data structures, and experience working with LLMs and AI agents are essential.

Skills: ai, llms, speech, low-latency, orchestration, reliability systems, python, algorithms, data structures, software engineering, ml models, voice agents

Summary: Candidates must have at least five years of experience applying AI/ML research to ship user-facing, production-grade products, possessing a strong software engineering background. Required expertise includes fine-tuning and deploying large language models, proficiency in prompt engineering, and comfort building scalable data-driven feedback loops.

Skills: predictive ai features, natural language detection, autosuggestions, intelligent prompt recommendations, code generation quality, fine-tuning llms, prompt engineering, data-driven feedback loops, context retrieval systems, software engineering, agent development, product implementation

Summary: Candidates must have extensive experience in AI/ML software engineering with multiple production launches, strong software engineering fundamentals, and proven experience with LLM application patterns like context engineering and vector search. Comfort operating in cloud environments like Azure, containers, and CI/CD is also required.

Skills: ai/ml software engineering, system design, testing, reliability, code review, apis, llm application patterns, context engineering, vector search, prompt design, structured outputs, evaluation, monitoring, cloud environments, azure, ci/cd

Summary: Candidates must possess a Bachelor's Degree in a relevant field and have 1–2 years of experience in AI, automation, software development, or data analytics. Essential technical requirements include hands-on experience with Microsoft Copilot/Copilot Studio and proficiency in Power BI, along with basic knowledge of Power Platform tools.

Skills: microsoft copilot, copilot studio, power automate, power bi, power apps, power platform, data modeling, prompt design, generative ai, python, dax, sql, javascript, analytical skills, problem solving, collaboration

Summary: Candidates must possess a Bachelor's or Master's degree in a quantitative field and demonstrate advanced expertise in Python, proficiency in GenAI tools like AWS Bedrock, and a full understanding of agentic systems and RAG implementation. Strong communication skills in C1 English are essential for international collaboration.

Skills: python, aws bedrock, rag systems development, agentic ai workflows, machine learning fundamentals, fastapi, knowledge graphs, information retrieval, llms, deep learning architectures, mlops, ci/cd, api development, nlp, data modeling, transcriptomics data analysis

Summary: Candidates must have successfully taken AI-powered systems from initial idea through production and at least one iteration cycle with real users, possessing strong engineering fundamentals for end-to-end ownership of services and data flows. Required experience includes working with LLM APIs, RAG architectures, and structured tool-calling, coupled with a bias toward action and comfort with ambiguity.

Skills: ai systems design, production deployment, engineering fundamentals, llm apis, embeddings, retrieval augmented generation, tool calling, agents, api stitching, webhooks, event systems, observability, workflow automation, experimentation, data flows, integrations

Summary: Candidates need 5+ years of hands-on experience in AI or applied data-intensive software engineering, with a proven track record of taking projects from idea to production. Proficiency in Python and hands-on experience with modern GenAI tooling like LangChain, RAG systems, and at least one major cloud platform are essential.

Skills: python, pinecone, huggingface, langchain, langsmith, crewai, agentic architectures, rag systems, azure, aws, gcp, client-facing work, mentoring, genai

Summary: Candidates must possess extensive experience with Python, including backend frameworks, deep understanding of AWS AI infrastructure, and experience using the OpenAI API for LLMs. A minimum of 2+ years in AI/ML engineering and 5+ years in full-stack engineering is required, along with U.S. citizenship and the ability to obtain a public trust clearance.

Skills: python, aws, fastapi, flask, openai api, llms, terraform, infrastructure-as-code, software architecture, system design, clinical design/delivery, problem-solving, communication, ai solutioning, vector databases, cloud platforms

Summary: Candidates must have a Bachelor's degree in Computer Science or a related field, coupled with over 5 years of software engineering experience primarily using Python and cloud platforms. A deep understanding of LLMs, proficiency in prompt engineering techniques, and hands-on experience with frameworks like LangChain and LangGraph are essential.

Skills: llm workflows, autonomous agents, mcp agents, fine-tuning, prompt optimization, rag pipelines, python, cloud platforms, langchain, langgraph, ai evaluation frameworks, benchmarking, agent orchestration, vector databases

Summary: Candidates must have over 5 years of experience building and deploying production AI/ML systems, including hands-on operationalization of LLM-based systems and experience with multimodal or vision systems. Strong proficiency in Python and experience with PyTorch or TensorFlow, along with distributed/cloud infrastructure for ML workloads, is required.

Skills: multimodal ai, llms, asr models, vision architectures, rag pipelines, prompt-based approaches, tool-using agents, inference systems, pytorch, tensorflow, ml lifecycle, gpu optimization, mlops, latency optimization, cost efficiency, safety guardrails

Summary: Candidates must have 2+ years of software development experience with high proficiency in Python, alongside proven expertise in building AI agents using frameworks like LangChain or CrewAI, focusing on agent design patterns. A demonstrable interest in cybersecurity is essential, though deep expertise is not strictly required.

Skills: python, langchain, crewai, reasoning patterns, tool orchestration, memory management, prompt engineering, rag, chain-of-thought processing, few-shot learning, sql, nosql, docker, aws, react, playwright

Summary: Candidates must have 2+ years of software development experience with high proficiency in Python, proven expertise in building AI agents using frameworks like LangChain or CrewAI, and hands-on experience with advanced LLM techniques such as RAG and prompt engineering. A demonstrable interest in cybersecurity is essential, alongside familiarity with databases, cloud deployment (AWS), and frontend skills like React.

Skills: python, langchain, crewai, reasoning patterns, tool orchestration, memory management, prompt engineering, rag, chain-of-thought processing, few-shot learning, sql, nosql, docker, aws, react, playwright

Summary: Candidates need 10 years of overall experience in technical roles, including a minimum of 1-2 years building and deploying generative AI solutions in enterprise settings. A Bachelor's degree or equivalent experience is required, along with proficiency in backend development using AI algorithms and specific technologies like Python, MLOps, and modern AI frameworks.

Skills: generative ai, agentic solutions, mlops, cicd, prompt templates, model evaluation, red-teaming, nist ai risk management framework, python, java, linux, node.js, apis, vector databases, rag, aws bedrock

Summary: Candidates must possess strong Python skills with over 5 years of experience building production backend or data engineering systems, alongside hands-on experience with LLM architectures like RAG and prompt engineering. Desired qualifications include fluency in Azure infrastructure, experience with search/retrieval systems, and familiarity with the MS365 ecosystem or workflow automation platforms.

Skills: python, llm apis, rag architectures, prompt engineering, evaluation frameworks, full-stack development, azure, semantic search, workflow automation, voice ai, node.js, typescript, data pipelines, api integrations, orchestration, llm agents

Summary: Candidates must possess deep hands-on software engineering expertise and platform-level thinking to build scalable, distributed ML systems, coupled with mastery of MLOps practices and cloud proficiency (GCP/AWS). Expert-level Python is required, along with familiarity with modern ML frameworks and orchestration tools like LangChain or LLamaIndex.

Skills: ai systems design, ml productionization, distributed software engineering, large language models, rag pipelines, agentic loops, evaluation frameworks, observability, python, mlops, kubernetes, docker, gcp, aws, langchain, llamaindex

Summary: Candidates must possess over 15 years in relevant engineering fields, including at least 5 years in senior/principal leadership roles and 3 years leading teams, with significant experience in client-facing environments. Essential technical expertise includes deep proficiency in agentic AI frameworks like LangChain, hands-on building of autonomous agent capabilities, and strong skills in Python, Spark, RAG pipelines, and LLMOps practices.

Skills: agentic ai, platform architecture, distributed systems, llmops, mlops, aiops, python, spark, langchain, rag, graphdb, vector databases, data ingestion, system scalability, ai governance, team leadership

Summary: Candidates must have 10+ years of software development experience, including time as an Individual Contributor and a mentor/lead, with hands-on experience building production applications using LLMs technologies like RAG or fine-tuning. A strong background in web development, engineering leadership, and proficiency with various database types (SQL/NoSQL) and TypeScript is required.

Skills: software development, llms, prompt engineering, rag, fine-tuning, agentic workflows, sql, nosql, postgres, cassandra, elasticsearch, graph databases, typescript, backend web development, rest apis, cloud environments

Summary: Candidates must be strong Python engineers capable of writing clean, production-ready code and possess real experience building with agent/orchestration frameworks like LangChain or LangGraph beyond simple tutorials. Success requires clear communication, high ownership, the ability to thrive in ambiguity, and a bias toward action in a fast-moving startup environment.

Skills: python, langchain, langgraph, agent-based workflows, system design, production deployment, monitoring, evaluation pipelines, backend software engineering, apis, data infrastructure, retrieval-augmented generation, rag, vector databases, llm-powered search, recommendation systems

Summary: Candidates must be strong Python engineers with deep experience in agent/orchestration frameworks like LangChain or LangGraph, capable of making architectural decisions under ambiguity. The role requires strong communication skills, a bias toward action, and the ability to lead, mentor, and grow an engineering team from within the codebase.

Skills: python, langchain, langgraph, agentic workflows, architecture, production systems, evaluation pipelines, backend software engineering, rag, vector databases, llm, technical leadership, mentoring, prioritization, system design, testing

Summary: Candidates must have experience with various programming languages like TypeScript, Python, and Go, alongside strong skills in cloud platforms such as Google Cloud, AWS, or Azure. Success requires technical decision-making ability, project leadership, team mentoring, and a background in building scalable systems with both front-end and back-end technologies.

Skills: full stack engineering, ai integration, system design, technical leadership, next.js, react, angular, fastapi, django, spring boot, .net, postgresql, bigquery, mongodb, redis, kafka

Summary: Candidates must have 1–3 years of hands-on experience building and deploying full-stack applications, specifically requiring Node.js and experience with modern UI frameworks like Angular (preferred). A U.S. citizenship is mandatory due to the required security clearance for this position.

Skills: full-stack engineering, ai-enabled systems, node.js, react, angular, vue, svelte, generative ai, agentic frameworks, front end, back end, aws connect, lex, lambda, s3, cloudwatch

Summary: Candidates must have significant hands-on experience with Supervised Fine-Tuning (SFT) of LLMs in production environments, moving beyond prompt-only approaches, and direct experience using OpenAI APIs or AWS Bedrock for post-training tasks. Strong proficiency in Python, ML frameworks like PyTorch, and experience operating ML systems in distributed production environments are required.

Skills: supervised fine-tuning (sft), large language models (llms), preference optimization, agentic llm systems, instruction tuning, rlhf, direct preference optimization, model evaluation, a/b testing, prompting, retrieval-augmented generation (rag), tool use, python, pytorch, aws bedrock, openai apis

Summary: Candidates must possess a minimum of 10 years of progressive leadership experience in areas like digital health or product development, including 10+ years in software engineering with at least 5+ years in AI/ML leadership roles. A Bachelor’s degree in a related field is required, with an advanced degree preferred, alongside deep technical expertise in ML algorithms, deep learning, NLP, and cloud AI services.

Skills: artificial intelligence, machine learning, ai strategy, model building, ethical ai, agile methodologies, user-centricity, design systems, cybersecurity governance, team leadership, python, tensorflow, pytorch, cloud ai services, generative ai, model interpretability

Summary: Candidates must have a minimum of 10 years of progressive leadership experience in digital health, product development, or related fields, including at least 5+ years in AI/ML leadership roles with a proven track record of delivering AI-driven products at scale. A Bachelor’s degree in a related field is required, with an advanced degree preferred.

Skills: artificial intelligence, machine learning, ai strategy, scalable ai models, ethical ai, agile methodologies, user-centricity, design systems, information security, team leadership, python, tensorflow, pytorch, scikit-learn, generative ai, llms

Summary: The ideal candidate must have strong backend engineering experience, preferably in Python, including designing scalable APIs and distributed systems, and hands-on experience building LLM applications using frameworks like LangChain. A systems-oriented approach with experience in observability, logging, and production monitoring tools is required, along with a track record of deploying AI features from prototype to production.

Skills: llm-powered agents, orchestration workflows, evaluation pipelines, retrieval-augmented generation, backend services, apis, observability, tracing, debugging tooling, latency optimization, scalability, ci/cd, deployment pipelines, python, distributed systems, langchain

Summary: Candidates must have proficiency in REST APIs (GET, POST, PATCH), Python or JavaScript (Node.js), JSON, and OAuth2/API key authentication, along with basic automation/scheduling skills. Experience with Claude API, OpenAI, LLMs, or specific platform APIs like ConnectWise is a bonus.

Skills: rest apis, python, javascript, node.js, json, oauth2, api key auth, claude api, openai, llm, connectwise, pax8, microsoft graph api

Summary: Candidates must have 6+ years of professional software engineering experience with a proven track record deploying AI/LLM systems into production, requiring strong Python expertise and proficiency in at least one of Node.js or Java. Essential technical skills include experience with microservices, Docker, Kubernetes, and MLOps pipelines on major cloud platforms like Azure, Databricks, or AWS.

Skills: python, node.js, java, fastapi, flask, django, docker, kubernetes, github actions, mlflow, azure ml, databricks, aws, etl, api integrations, agile

Summary: Candidates must be strong conceptual thinkers capable of structuring complex business challenges into data problems, with a passion for LLMs and real-world AI applications. Required experience includes a PhD with 3+ years of industry experience or a Bachelor's/Master's with 7+ years, plus at least 1 year of team management and hands-on experience building AI applications.

Skills: large language models, data science, team leadership, ontology transformation, actionable insights, agentic applications, project management, model development, production-ready systems, system architecture, prompt engineering, advanced analytics, unstructured data transformation, mentoring

Summary: Candidates must possess a minimum of 2 years of software engineering experience along with 2+ years working hands-on with AI/ML systems, including experience with LLMs and/or VLMs. Essential technical skills include strong Python programming, familiarity with AI/ML frameworks like PyTorch or TensorFlow, and proficiency with foundation model APIs and prompt engineering.

Skills: large language models, vision language models, speech recognition, conversational ai, document understanding, ocr, human-in-the-loop, multimodal ai, model fine-tuning, prompt engineering, python, pytorch, tensorflow, foundation model apis, cloud-based ai infrastructure, model deployment

Summary: Candidates must possess over 7 years of hands-on engineering experience, with a strong requirement for leading greenfield or 0-to-1 product builds involving AI systems or data-intensive architectures. Proficiency in Python and experience integrating LLMs, vector databases, and cloud services are essential for this role.

Skills: greenfield development, zero to one products, ai systems, automation, workflow engines, data intensive architectures, cloud deployment, aws, system design, python, llms, embeddings, prompt orchestration, model evaluation, ci/cd, observability

Summary: Candidates must have a BSc or MSc in a relevant technical field or equivalent experience, along with over 5 years of experience deploying machine learning systems in production. Proven expertise in NLP techniques, LLM fine-tuning, deep learning frameworks like PyTorch/TensorFlow, and advanced Python programming are required.

Skills: agentic workflows, large language models, ml/nlp products, model training, model evaluation, model optimization, deployment, agentic reasoning, autonomous system design, deep learning frameworks, python programming, ml pipelines, conversational ai, multi-agent systems, data quality management, analytical thinking

Summary: Candidates need over 6 years of software engineering experience, including significant expertise in C#/.NET, Python, and PostgreSQL, along with proficiency in TDD and automation testing. Required cloud expertise includes AWS, GCP, or Azure, and experience with SaaS application development and distributed systems is essential.

Skills: c#, .net, python, postgresql, tdd, xunit, postman, terraform, pulumi, kafka, docker, kubernetes, ci/cd, jenkins, aws, gcp

Summary: Candidates must possess over 2 years of deep experience in systematic prompt engineering and have solid software engineering skills in Python and Azure cloud infrastructure. Proficiency in Swiss German for audio sanity checks and High German for written output evaluation is mandatory, alongside a pragmatic startup mindset.

Skills: prompt engineering, pipeline management, llm integration, model evaluation, python, azure, clinical documentation, swiss german, high german, experimentation, data labeling, saas, communication

Summary: Candidates must have practical experience in LLM Engineering, specifically RAG and Agentic RAG, along with solid software engineering skills, ideally in a cloud-native environment. A very good command of spoken and written German is required for effective communication with German-speaking customers and project teams.

Skills: llm engineering, rag, agentic rag, evaluation methods, software engineering, apis, testing, logging, monitoring, cloud-native, azure ai services, azure ai search, data privacy, data governance, prompt engineering, guardrails

Summary: Candidates must have experience in AI/Machine Learning Engineering roles with solid software engineering skills, specifically requiring Python and hands-on experience deploying ML models to production. A strong understanding of MLOps, experience with cloud platforms (Azure preferred), and knowledge of containerization technologies like Docker and Kubernetes are essential.

Skills: ai engineering, machine learning engineering, software engineering, python, ml model deployment, mlops, cloud platforms, azure, docker, kubernetes, ai systems design, llms, genai, communication skills, consulting mindset

Summary: Candidates must have experience in AI/Machine Learning Engineering roles, solid software engineering skills (Python required), and hands-on experience deploying ML models into production environments. A strong understanding of MLOps, experience with cloud platforms (Azure preferred), and knowledge of containerization technologies like Docker and Kubernetes are essential.

Skills: ai engineering, python, mlops, cloud platforms, azure, docker, kubernetes, software engineering, ml model deployment, ai pipelines, ai governance, llms, genai, communication skills, consulting mindset

Summary: Candidates must have 4-6 years of AI project experience, including at least 2+ years implementing autonomous agents or copilots, with proven experience using agent frameworks like LangChain or AutoGen. Solid proficiency in Python, asynchronous programming (FastAPI, asyncio, Pydantic), and experience with vector stores and RAG architectures are required.

Skills: ai engineer, intelligent agents, llms, langchain, llamaindex, autogen, crewai, mcp, a2a, python, fastapi, rag, nlp, mlops, docker, kubernetes

Summary: Candidates must have at least five years of experience applying AI/ML research to ship user-facing, production-grade products, possessing a strong software engineering background. Proficiency in fine-tuning and deploying large language models, adeptness at prompt engineering, and comfort building scalable data-driven feedback loops are essential.

Skills: predictive ai features, natural language detection, autosuggestions, intelligent prompt recommendations, code generation quality, fine-tuning, prompt engineering, data-driven feedback loops, context retrieval systems, software engineering, large language models, agent development, production deployment

Summary: Candidates must have 8+ years of total experience, including at least 4+ years in AI/ML solution design and implementation, with preferred demonstrated GenAI/LLM delivery experience. Required technical proficiency includes Python, ML frameworks (PyTorch, Scikit-learn), cloud AI/ML services (AWS, Azure, GCP), and familiarity with RAG architectures and MLOps tooling.

Skills: ai/ml solution design, cloud platforms, generative ai, rag, agentic workflow, llmops/mlops, pytorch, hugging face, scikit-learn, langchain/langgraph, data preprocessing, vector search, aws, azure, google cloud vertex ai, databricks

Summary: The ideal candidate is a fast-moving, product-minded full-stack engineer who is autonomous and forward-thinking about AI's potential. They must be comfortable working hands-on with technologies like Python, LLMs, and cloud platforms while ensuring AI features meet enterprise standards for privacy and fairness.

Skills: prompt design, evaluation pipelines, llms, fine-tuning, model evaluation, cost-optimization, latency, caching, python, openai api, pytorch, hugging face, postgresql, gcp, poetry

Summary: Candidates must have strong full-stack development experience in TypeScript or Python, coupled with practical experience building intelligent workflows using LangChain, various AI models, vector databases, and advanced RAG techniques. Additional valued skills include cloud deployment knowledge (AWS, Docker, Kubernetes) and deep understanding of CI/CD and testing methodologies.

Skills: typescript, python, langchain, vector databases, rag techniques, tool calling, aws, docker, kubernetes, ci/cd, github actions, travisci, codebuild, testing, model fine-tuning, reinforcement learning

Summary: Candidates must have over four years of technical experience in software development or platform engineering, demonstrating proficiency with Microsoft Power Platform, Copilot Studio, and Azure services like Azure OpenAI. Essential skills include basic coding in Python or PowerShell and familiarity with RAG architectures.

Skills: microsoft power platform, generative ai, copilot studio, azure openai, power automate, power apps, azure functions, logic apps, storage, python, powershell, api integrations, rag, typescript, javascript, c#

Summary: Candidates need a strong CS or ML educational background (BSc, MSc, or PhD) with solid software engineering principles and experience building and deploying large-scale ML systems via APIs, especially in cloud environments like AWS. Direct experience deploying AI systems for external customers, strong communication skills, and a delivery-oriented mindset are essential.

Skills: generative models, deployment, integration, optimization, infrastructure expertise, api access, model serving, cloud infrastructure, aws, gcp, azure, docker, kubernetes, ci/cd, customer facing, technical communication

Summary: Candidates must have a strong CS or ML educational background (BSc, MSc, or PhD) with solid software engineering principles and experience building systems that access large models via APIs. Essential requirements include significant experience deploying and maintaining large-scale model serving infrastructure, fluency in cloud platforms like AWS, and proven customer-facing delivery orientation.

Skills: generative models, deployment, integration, optimization, infrastructure expertise, api access, model serving, cloud infrastructure, aws, gcp, azure, docker, kubernetes, ci/cd, customer facing, technical communication

Summary: Candidates need a Bachelor's degree (Master's preferred) and experience with Data Science, Artificial Intelligence, and Large Language Models, along with object-oriented programming skills and working knowledge of DataBricks. A minimum of 2 years in a technical role, leading projects, is required, increasing to 2+ years if the candidate holds a Master's degree.

Skills: data science, artificial intelligence, large language models, object-oriented programming, databricks, data analytics, business intelligence, python, r, sql, domo, tableau, powerbi, generative ai, project management, client facing

Summary: Candidates must have over 5 years of production Python experience at scale and strong AWS expertise, along with familiarity with agentic AI architectures like LangChain or AutoGen. The role requires comfort working across the stack, including shipping small UI features using React/Next.js and familiarity with relational databases.

Skills: python, aws, lambda, s3, cloudwatch, llms, agentic ai architectures, langgraph, langchain, autogen, llamaindex, infrastructure as code, cdk, terraform, ci/cd, typescript

Summary: Candidates must possess over 5 years of production Python experience at scale, strong AWS expertise across several services, and familiarity with relational databases. Required experience also includes agentic AI architectures, data pipeline development (streaming/batch), Infrastructure as Code, and production SaaS experience including on-call participation.

Skills: python, aws, lambda, dynamodb, s3, glue, athena, mysql, postgresql, oracle, agentic ai architectures, data pipeline development, infrastructure as code, cdk, terraform, github actions

Summary: Candidates must have 2+ years of software engineering experience and demonstrate an ownership mentality for AI systems, taking responsibility for their production value. A key requirement is experience building end-to-end AI systems powered by LLMs, not just calling APIs, alongside strong engineering fundamentals in clean, maintainable Python.

Skills: software engineering, ai systems operation, system design, llm applications, agent design, retrieval, evaluation frameworks, python, debugging, testing, system integration, stakeholder communication, prompt engineering, ai tool usage

Summary: Candidates must have proven experience developing image/video processing and computer vision solutions, along with 3+ years in a machine learning engineer/researcher role. A Master's or PhD in a relevant technical field is preferred, though equivalent industrial experience with a Bachelor's degree is acceptable.

Skills: computer vision, machine learning, deep learning, algorithm design, image processing, video processing, python, scikit-learn, scipy, matplotlib, pytorch, data structures, data modeling, software architecture, communication, experimentation

Summary: Candidates must have proven experience developing image/video processing and computer vision solutions, along with 3+ years in a machine learning engineer/researcher role. Proficiency in common Python ML frameworks, strong software architecture understanding, and excellent English communication skills are required.

Skills: computer vision, machine learning, deep learning, algorithm design, image processing, video processing, python, scikit-learn, scipy, matplotlib, pytorch, data structures, software architecture, experimentation, model testing, data processing

Summary: Candidates must possess 10+ years in software engineering or machine learning, including at least 4 years managing ML/AI teams, with a proven track record of shipping production ML/LLM systems at scale. Deep technical expertise in ML frameworks, LLM technologies, modern MLOps, and cloud infrastructure on Azure is required, alongside strong strategic leadership and communication skills.

Skills: ai engineering strategy, technical roadmap, team leadership, ml/llm systems, proof-of-concept development, production deployment, mlops, azure, pytorch, tensorflow, llm technologies, prompt engineering, rag, architecture decisions, responsible ai, executive communication

Summary: Candidates must have at least two years of experience in both engineering management and Computer Vision, including a demonstrated track record of defining and implementing roadmaps that incorporate experimental R&D. Firsthand experience training Computer Vision models and experience at early-stage startups are also required.

Skills: computer vision, engineering management, strategy, roadmap definition, team leadership, hiring, onboarding, performance management, cross-functional collaboration, technical leadership, ai, machine learning, r&d, model training

Summary: Candidates must possess a Bachelor's degree (Master's preferred) and have experience with Data Science, Artificial Intelligence, and Large Language Models, along with object-oriented programming skills. A minimum of 2 years in a technical role, leading projects, is required, increasing to 2+ years if holding a Master's degree.

Skills: data science, artificial intelligence, large language models, object-oriented programming, databricks, data analytics, business intelligence, python, r, sql, domo, tableau, powerbi, generative ai, project leadership

Summary: Candidates must possess a minimum of 15 years of server and/or AI/ML accelerator hardware design experience, including at least 5 years in AI Infrastructure system design, with strong knowledge of AI training/inference workloads and associated subsystems. A BS in Electrical Engineering is required, with an MSEE preferred, alongside successful experience architecting and delivering server/storage/converged products to market.

Skills: ai/ml system architecture, system design, technology research, hardware design, ai training workloads, ai inference workloads, compute subsystems, connectivity, power subsystems, cooling subsystems, platform solutions, technical relationships, rfq responses, trade-off analysis, system requirements documents, customer interface

Summary: Candidates must possess advanced expertise in generative AI coupled with strong programming skills in languages like Python, TypeScript, PHP, or Rust, along with hands-on experience deploying and optimizing LLMs. Familiarity with Unix systems, API protocols, DevOps practices, and workflow automation tools like n8n is also required.

Skills: generative ai, python, typescript, php, rust, llms, api integration, linux, devops, kubernetes, docker, prompt engineering, ci/cd, observability, workflow automation, openstack

Summary: Candidates should have 8+ years of experience in software engineering or applied machine learning, focusing on building real-world AI/ML systems, or equivalent experience in healthcare-specific AI solutions. Proficiency in Python backend engineering, solid understanding of embeddings/embedding databases, and experience deploying cloud-native applications on AWS are required.

Skills: artificial intelligence, machine learning, python, language technologies, voice technologies, agentic ai, ml/ai architecture, sdlc ownership, backend services, technical leadership, aws bedrock, nlp, embeddings, embedding databases, cloud-native applications, technical mentorship

Summary: Candidates must have at least two years of experience in software engineering and/or machine learning applied in production, including hands-on experience with LLM applications, prompt engineering, and evaluation frameworks. Required technical expertise includes working with vector or graph databases, document chunking, embedding models, and familiarity with metadata-based retrieval and re-ranking strategies.

Skills: llm, rag systems, vector search, hybrid retrieval, reranking, contextual compression, agent systems, information extraction, information storage, information retrieval, mlops, prompt engineering, model evaluation, graph databases, embedding models, ci/cd

Summary: Candidates must have five or more years of software engineering experience, including at least two years building AI-powered systems in production, with strong proficiency in Python and experience integrating LLM APIs. A deep understanding of LLM behavior, prompt engineering, RAG patterns, and experience building user-facing AI features are essential.

Skills: python, llm apis, prompt engineering, retrieval-augmented generation, evaluation techniques, saas products, ruby on rails, vue, node.js, react, llm orchestration, agentic workflows, observability, nlp, information retrieval, decision support systems

Summary: Candidates need a Bachelor's degree preferred in Computer Science/Artificial Intelligence or equivalent experience, with 6+ years in data/analytics roles, including at least 3 years in analytical application development. Essential technical requirements include experience building and deploying LLM models using MS Azure, practical experience with NLP/LLMs, and proficiency with tools like Python, SQL, Docker, and Kubernetes.

Skills: generative ai, machine learning, llmops, llms, embedding pipelines, retrieval frameworks, data processing pipelines, ci/cd pipelines, docker, kubernetes, mlflow, python, nltk, huggingface, sql, snowflake

Summary: Successful candidates must have extensive software engineering experience, ideally with hands-on game development and production systems, alongside strong experience with Unity3D and mobile game pipelines. Proficiency is required in training and deploying ML models using PyTorch, practical experience with generative models and LLMs, and familiarity with Google Vertex AI.

Skills: software engineering, game development, unity3d, pytorch, image generation models, multimodal models, llms, sft, dpo, grpo, google vertex ai, llm agents, adaptive systems, technical documentation, architecture, machine learning

Summary: Candidates must have over 5 years of experience building production-grade frontend applications, demonstrating a strong ability to rapidly design and iterate product prototypes while deeply understanding customer problems. Essential technical expertise includes HTML, CSS, TypeScript, modern frontend frameworks like React, and hands-on experience with AI-driven coding tools.

Skills: autonomous agents, llms, generative ai, agent tooling, context pipelines, vector search, evaluation stacks, data privacy, red-team testing, typescript, react, frontend development, responsive design, accessibility, playwright, jest

Summary: Candidates need 12+ years in cloud software development with 5+ years specializing in AI/ML, requiring expertise in generative AI models, deep learning frameworks (TensorFlow, PyTorch, JAX), and programming languages like Python. A Bachelor's, Master's, or Ph.D. in a related technical field is required, along with strong skills in MLOps and system design.

Skills: generative ai, deep learning, tensorflow, pytorch, jax, python, transformer architectures, model training, fine-tuning, prompt engineering, ml ops, docker, kubernetes, data engineering, system design, ci/cd

Summary: Candidates should be currently pursuing or recently finished an advanced degree in a related field, preferably a PhD, with demonstrated interest or experience in AI and healthcare technology research. Strong foundational knowledge in generative AI models, architectures, and experience with ML/DL models and cloud services are required.

Skills: generative ai, llms, ml tools, ai governance, model development, architecture design, database management, communication, collaboration, technology trends, deep learning, rag models, llm fine-tuning, cloud services, data pipelines, vector databases

Summary: Candidates must possess a Bachelor's degree in a relevant technical field and have at least 5 years of experience in SRE, DevOps, or Systems Engineering, including 2 years applying AI/ML techniques in operational contexts. A mandatory requirement for consideration is holding an active Secret Clearance.

Skills: site reliability engineering, automation, orchestration, artificial intelligence, machine learning, aiops, observability, anomaly detection, incident management, root-cause analysis, automation playbooks, infrastructure-as-code, knowledge management, predictive reliability engineering, data analysis, distributed systems

Summary: Candidates should be engineers who prioritize evaluation as foundational for reliable AI systems, possessing an evaluation-first mindset and data-driven rigor. Required experience includes shipping production software in complex systems, building and deploying LLM-powered features, and implementing evaluation frameworks, with familiarity in TypeScript, Python, and Postgres.

Skills: evaluation infrastructure, unified platform, automated pipelines, production feedback loops, ml engineering, observability, quality assurance, llms, langsmith, langgraph, retrieval systems, model evaluation, guardrails, typescript, python, postgres

Summary: Candidates must have over two years of experience shipping production-grade AI products, demonstrating deep hands-on experience with agents, RAG pipelines, or self-healing workflows. Strong prompt engineering skills and proven operational excellence in managing LLM system latency, reliability, and cost are essential.

Skills: llms, agent architecture, prompt frameworks, rag, tool-use, orchestration, persistent memory, eval loops, ai safety, voice ai, multi-modal reasoning, function calling, self-healing workflows, langchain, react, openai tools

Summary: Candidates must possess strong AI/ML fundamentals and an evaluation-driven mindset, focusing on metrics and quantitative measurement rather than pure research.

Skills: ai/ml fundamentals, evaluation-driven approach, production ai systems, experimentation, prompt design, agent workflows, metric design, kpis, system design, golang, python, postgres, redis, ranking systems, itinerary optimization, llm reasoning

Summary: Candidates must possess a strong understanding of generative AI architectures and prompt engineering patterns, along with essential coding skills in Python and familiarity with frameworks like TensorFlow, PyTorch, or Hugging Face. Desirable skills include familiarity with security automation concepts and knowledge of privacy controls in AI systems.

Skills: generative ai, prompt engineering, model fine-tuning, python, tensorflow, pytorch, hugging face, git, ci/cd pipelines, langchain, langgraph, javascript, security automation, cybersecurity principles, proof-of-concept development, security controls

Summary: Candidates must have 3-5+ years of enterprise software development experience, proficiency in multiple programming languages, and strong knowledge of software engineering design patterns and architecture. Essential AI skills include enterprise LLM integration, multi-agent orchestration, mastery of RAG pipelines with vector databases, and experience deploying models securely on major cloud platforms.

Skills: ai systems design, llm integration, agentic swarm coding, multi-agent workflows, rag systems, vector databases, polyglot microservices, cloud deployment, ci/cd pipelines, adversarial testing, software architecture, python, javascript, typescript, .net, c#

Summary: Candidates should be experienced Staff-level engineers with a strong background in applied AI, agentic programming, and LLM orchestration frameworks like LangChain or CrewAI. Comfort working in ambiguous, fast-changing environments and skill in building evaluation frameworks for AI systems are essential requirements.

Skills: applied ai, agentic programming, llm-powered automation pipelines, llm orchestration frameworks, langchain, langgraph, crewai, agent orchestration, tool-use systems, evaluation frameworks, systems thinking, agent-augmented software, data access, query parsing, incident triage, metric queries

Summary: Candidates must have 5+ years of experience in AI/ML engineering, strong Python and backend skills, and a solid foundation in statistics and ML theory. Proven hands-on experience building with LLMs (including RAG, fine-tuning, etc.) and a track record of taking ML models from prototype to production are essential.

Skills: python, backend engineering, statistics, ml theory, llms, prompt engineering, rag, embeddings, fine-tuning, agent orchestration, voice ai, asr, tts, conversational ai, graphql, kubernetes

Summary: Candidates must possess 10+ years of relevant engineering experience, including technical leadership roles and a proven track record leading large-scale AI/ML initiatives in a product-driven setting. Significant experience in model deployment, development, training, fine-tuning, and evaluation is required, along with a strong user focus and passion for mentoring.

Skills: ai, ml, technical leadership, product minded, communication, prototyping, evaluation, deployment, agent systems, retrieval strategies, planning strategies, prompt management, human-in-the-loop, mentoring

Summary: Candidates must have a degree in Machine Learning, Computer Science, AI, Mathematics, or a comparable qualification, along with solid knowledge in ML/AI technology and practical software development experience with an interest in full-stack development. Proficiency in Python, web frameworks, databases like PostgreSQL, and ML libraries like PyTorch/Transformers, along with very good German (C1) and good English (B2) skills, is required.

Skills: python, llm apis, rag, fine-tuning, evaluation, node.js, nestjs, rest, graphql, prisma, react, next.js, typescript, tailwind, postgresql, aws

Summary: The ideal candidate must be a product-minded engineer combining deep expertise in LLMs and agentic systems with strong software engineering skills to deliver reliable, commercially impactful products. Proven experience building and shipping production-grade AI or agentic systems, ideally in high-stakes domains, is required.

Skills: agentic ai workflows, llm expertise, software engineering, production-grade systems, tool use, orchestration, planning, memory, structured outputs, evaluation pipelines, llmops, retrieval pipelines, context pipelines, auditability, product mindset, experimentation

Summary: Candidates must have 3+ years of hands-on software engineering experience building production systems, strong Python skills, and practical experience with LLMs, RAG systems, and containerization in secure environments. Eligibility for the highest level of national and NATO security clearances is mandatory.

Skills: python, go, typescript, rust, llms, prompt engineering, rag systems, langchain, langgraph, vector databases, docker, apis, data pipelines, kubernetes, linux, communication

Summary: Candidates must possess strong product management experience owning developer-facing products, coupled with a strong technical foundation including hands-on software development and comfort using the command line daily. Familiarity with the AI coding assistant landscape and experience defining product strategy for CLI-first experiences are also essential.

Skills: product strategy, roadmap definition, cli experience, ide extensions, ai developer tools, devsecops platform, gitlab duo, command line workflows, product execution, developer experience, product quality, iterative delivery, competitive analysis, software development, b2b saas, developer platforms

Summary: Candidates must have a minimum of two years of professional software development experience, demonstrating strong proficiency in JavaScript and TypeScript, along with hands-on experience using React and/or Next.js. A solid understanding of REST APIs, relational databases, and comfort owning features end-to-end are required, alongside daily use of AI coding assistants.

Skills: javascript, typescript, react, next.js, rest apis, postgresql, ai coding assistants, frontend development, backend development, api implementation, testing, architecture, code reviews, real-time systems, conversational ai, voice ai

Summary: Candidates must have a Bachelor's degree in a related field and over 5 years of experience in a DevOps role, demonstrating strong proficiency with major cloud providers and scripting languages like Python, Bash, or Go. Required technical expertise includes extensive hands-on experience with Kubernetes, Helm, Docker, and Infrastructure as Code tools like Terraform, alongside exposure to AI/ML systems.

Skills: devops, ai engineering, ci/cd, llm orchestration, vector databases, kubernetes, helm, istio, aws, azure, gcp, oci, python, bash, go, terraform

Summary: Candidates should have 1-3 years of software engineering experience, proficiency in writing clean Python code, and be capable of full-stack work, including backend development and familiarity with frontend technologies. A strong desire to learn the healthcare domain and leverage AI development tools is essential.

Skills: software engineering, backend development, apis, data integration, ai/ml systems, python, javascript, typescript, react, cloud infrastructure, aws, gcp, azure, ai-native development, self-directed, communication

Summary: Bachelor's degree in Computer Science or related field, or 3+ years of relevant experience is required. Familiarity with API development and past experience with AI, specifically Large Language Models, is preferred.

Skills: ai, large language models, api development, integration, saas platforms, problem-solving, analytical skills, communication, collaboration, typescript, aws, data pipelines, automation, research, development, compliance

Summary: Candidates should have proficiency in Python or Typescript and experience with LLM tooling. A deep understanding of agent design patterns and responsible AI practices is also required.

Skills: python, typescript, llm tooling, langchain, llamaindex, guardrails, mcp, agents sdk, agent design patterns, observability, debugging, human-in-the-loop feedback systems, cloud environments, devops tools, architectural design, systems design

Summary: Candidates should have 3+ years of experience in building production systems with Python/Django and hands-on experience with LLMs/SLMs in fraud or anomaly detection contexts. A practical mindset for rapid iteration and a track record of optimizing ML inference are also essential.

Skills: python, django, postgres, llms, slms, fraud detection, anomaly detection, data ingestion, machine learning, feature engineering, model optimization, latency reduction, observability, api integration, data pipelines, quantization

Summary: Candidates should have a Bachelor’s or Master’s degree in a technical discipline and demonstrated experience as a backend software engineer. Strong programming skills, particularly in Python, and a proven ability to design scalable backend systems are essential.

Skills: python, backend development, apis, cloud infrastructure, container orchestration, ai ecosystem, data pipelines, microservices, automation, problem solving, communication, scalability, system design, integration, documentation, experimentation

Summary: Candidates should have a Bachelor's or Master's degree in a technical discipline and experience as a backend software engineer. Strong programming skills, particularly in Python, and knowledge of AI ecosystem components are essential.

Skills: ai integration, backend development, api development, cloud infrastructure, data processing, microservices, python, ruby, go, scalability, automation, problem solving, communication, ai ecosystem, experimentation, documentation

Summary: Candidates should have at least 3 years of experience in financial services and 2 years specifically in building GenAI solutions. A bachelor's degree in a related field is required, along with expertise in AWS AI/ML solutions.

Skills: machine learning, aws ai/ml stack, llm frameworks, voice ai solutions, document processing, emotional intelligence, call center optimization, advanced english, comprehensive documentation

Summary: Candidates should have 3+ years of experience in applied ML or data science, particularly in recommendation and personalization systems. Strong proficiency in Python and modern ML frameworks is essential, along with experience in LLMs and agentic workflows.

Skills: machine learning, data science, python, pytorch, tensorflow, jax, hugging face, llms, embeddings, natural language processing, voice interactions, a/b testing, mlops, reinforcement learning, preference modeling, human-centered ai

Summary: Candidates should have over 10 years of experience in software development and a deep knowledge of LLM architecture and prompt engineering. Familiarity with cybersecurity and cloud infrastructure security is a plus.

Skills: software engineering, architecture expertise, llm architecture, prompt engineering, vector database workflows, microservices architecture, containerization, event-driven systems, api design, distributed systems, problem-solving, analytical skills, communication skills, leadership abilities, mentoring, cybersecurity

Summary: Candidates should have 10+ years of software engineering experience and at least 3+ years focused on AI/ML solutions. Proficiency in Python and experience with modern AI services and MLOps tools are essential.

Skills: software engineering, ai/ml solutions, cloud services, vector databases, python, databricks, mlops, rest apis, nlp, responsible ai, problem-solving, deep learning, ci/cd pipelines, success metrics, nodejs, customer loyalty

Summary: Candidates should be self-starters with deep machine learning expertise and strong engineering fundamentals. A background in frontier labs or notable independent achievements in AI research is preferred.

Skills: machine learning, pytorch, jax, data pipelines, model training, model evaluation, collaboration, prototyping, production deployment, research, engineering fundamentals, autonomy, project collaboration, multimodal datasets, ai research, frontier-scale models

Summary: At least 6 years of experience in AI/ML engineering with proficiency in Python and cloud environments. Strong understanding of MLOps best practices and experience in client-facing roles are essential.

Skills: machine learning, ai engineering, python, javascript, go, pytorch, tensorflow, aws, gcp, azure, mlops, data infrastructure, client engagement, model deployment, data pipelines, neural networks

Summary: Candidates should have extensive experience building and using web APIs, as well as experience with relational databases, particularly PostgreSQL. Familiarity with analytics tools and technologies, streaming analytics, and experience using LLMs or traditional ML techniques is also desired.

Skills: web apis, postgresql, analytical sql, snowflake, google bigquery, duckdb, spark sql, pandas, polars, streaming analytics, data processing pipelines, flink, beam, spark, machine learning, llms

Summary: Candidates should have deep technical expertise in machine learning and a passion for developing novel systems. They should thrive in research-oriented environments and care about measurable impact in students' learning outcomes.

Skills: machine learning, ai systems, research, data analysis, collaboration, experimentation, human-centered design, engineering solutions

Summary: The ideal candidate should have strong analytical and problem-solving skills, along with experience in AI/ML and LLMs. Proficiency in Python and familiarity with document processing tasks are also beneficial.

Skills: problem-solving, collaboration, enthusiasm, ai, ml, llms, prompt engineering, agentic systems, document processing, python, mypy, asyncio

Summary: The role requires strong engineering fundamentals and at least 3 years of experience in software development with a focus on machine learning. Familiarity with large language models and experience with Python backend development are essential.

Skills: python, machine learning, natural language processing, large language models, apis, backend development, fastapi, aws, data evaluation, agent systems, restful api design, tool orchestration, collaboration, evaluation, monitoring, dynamodb

Summary: Candidates should have over 5 years of experience in Data Engineering or Software Engineering, with proven leadership skills. Strong technical skills in Python and cloud infrastructure, along with a deep understanding of AI principles, are essential.

Skills: data engineering, ai engineering, python, cloud infrastructure, aws, apache airflow, docker, git, ai principles, team leadership, automation, collaboration, technical leadership, scalable solutions, stakeholder alignment, experimentation

Summary: Candidates should have demonstrated experience in building and deploying AI applications and a strong understanding of backend architecture principles. Experience with RESTful APIs and database technologies is also required.

Skills: python, fastapi, sql, restful apis, ai applications, backend architecture, database technologies, kubernetes, terraform

Summary: Candidates should have at least two years of experience in machine learning, particularly in NLP, and a solid understanding of statistical techniques. Proficiency in Python, SQL, and cloud-based software architecture is also required.

Skills: machine learning, nlp, python, sql, nosql, data structures, data modelling, software architecture, cloud architecture, statistical techniques, recommender systems, llm, github, neurosymbolic ai, communication skills, creativity

Summary: Strong Python engineering skills and hands-on experience with LLMs are essential. You should also be comfortable working with ambiguity and have an instinct for simplifying business processes.

Skills: python engineering, llms, prompt design, retrieval pipelines, business process design, automation, clear communication, problem solving

Summary: Candidates must have at least 7 years of experience in AI, particularly in generative AI technologies. A strong background in project management and team leadership in Data Science is essential.

Skills: ai engineering, data science, project management, generative ai, nlp, llm, vlm, rag, technical consulting, team management, communication, leadership, problem solving, innovation, cloud, mlops

Summary: Candidates should have a bachelor's degree in a technical field and strong programming skills, particularly in Python or JavaScript/TypeScript. Additionally, 3+ years of experience in production software and 2+ years with LLMs or AI APIs are required.

Skills: python, javascript, typescript, llms, ai apis, rag, agent workflows, evaluation, performance optimization, prompt engineering, tool use, multi-step agent workflows, failure handling, communication skills, technical discussions

Summary: Candidates should have several years of experience in AI or Machine Learning roles with a focus on LLMs and GenAI in production. Strong Python skills and familiarity with GenAI frameworks are essential.

Skills: ai engineering, machine learning, llms, genai, python, pytorch, langchain, llamaindex, production systems, cloud environments, mlops, observability, testing, lifecycle management, communication, impact-driven mindset

Summary: Candidates should have 5+ years of AI modeling experience in the self-driving car industry or a PhD with 3+ years of relevant experience. A proven track record in developing and deploying large-scale ML models and familiarity with modern ML architectures is essential.

Skills: machine learning, ai modeling, robotics, diffusion models, autoregressive models, reinforcement learning, model debugging, performance optimization, python, pytorch, docker, nodejs, typescript, model evaluation, data strategies, training pipelines

Summary: Candidates should have strong Python engineering fundamentals and hands-on experience with core components of agentic systems. Familiarity with various libraries and tools, as well as the ability to evaluate AI outputs, is also required.

Skills: python, agentic systems, api integrations, orchestration techniques, fastapi, pydantic, pandas, redis, ai-augmented coding tools, observability tools, context engineering, machine learning, cloud infrastructure, debugging, monitoring, evaluation techniques

Summary: Candidates should have over 5 years of experience in building internal tooling and integrating LLMs into workflows. A strong understanding of CI/CD processes and excellent communication skills are also required.

Skills: developer productivity, ai workflow patterns, internal tools development, ci/cd automation, llm integration, structured workflows, cross-functional communication, product-thinking mindset

Summary: Candidates should have extensive experience in Developer Relations, particularly in open-source platforms, and a strong technical background in distributed systems and machine learning. They must also be able to communicate complex concepts effectively and engage with diverse developer communities across EMEA.

Skills: developer advocacy, distributed systems, machine learning, ai infrastructure, technical communication, community engagement, public speaking, content creation, software development, technical writing, event planning, collaboration, feedback gathering, networking, reference implementations, demos

Summary: Candidates should have a background in software engineering and be excited about LLMs and AI agents. Experience with full stack or backend Python development is essential, along with proven experience in delivering complex software systems.

Skills: ai engineering, software engineering, python development, llms, ai agents, agentic architectures, multi-agent systems, retrieval-augmented generation, ocr pipelines, apis, distributed systems, ci/cd, observability tools, graphql, websockets

Summary: Candidates should possess strong software engineering fundamentals in Python and ideally have a Ph.D. or MSc in a relevant technical field, coupled with over 7 years of experience in AI/ML Engineering, particularly building LLMs and Agentic Systems from development to production. Hands-on experience with LLM frameworks and AI platforms is valued.

Skills: artificial intelligence, machine learning, in-context learning, prompt engineering, retrieval augmented generation, tool calling, llm agents, eval-driven development, model-based critiques, semantic accuracy benchmarks, software engineering, python, langchain, langgraph, llamaindex, langsmith

Summary: Candidates should possess 5+ years of experience in Machine Learning and AI Engineering, preferably building with LLMs and Agentic Systems from development to production. Strong software engineering fundamentals and proficiency in Python are mandatory, with an advanced degree in a relevant technical field being highly valued.

Skills: artificial intelligence, machine learning, in-context learning, prompt engineering, retrieval augmented generation, tool calling, llm agents, eval-driven development, llm frameworks, langchain, langgraph, llamaindex, langsmith, ai platforms, databricks, mlfow

Summary: Candidates must have a minimum of two years of hands-on experience in AI/ML engineering, demonstrating proficiency with LLMs and applying AI/ML to complex, unstructured problems. Comfort working across the full stack, from prompt engineering to Kubernetes deployment, is required.

Skills: agentic systems, llms, model architectures, prompt engineering, vector db tuning, kubernetes deployment, api design, microservices, high-concurrency, information extraction, fine-tuning strategies, inference pipelines

Summary: Candidates need extensive technical aptitude, practical experience in managing technical systems, and proven leadership skills to mentor teams and manage complex projects through the full SDLC. A Bachelor's degree in Computer Science plus twelve years of engineering experience, including five years in leadership, is required.

Skills: ai engineering, technology leadership, application development, infrastructure management, security, network management, developer experience, cross-functional collaboration, platform capabilities, software engineering organization, data utilization, automation, roi analysis, saas technologies, cloud architecture, operational compliance

Summary: Candidates must have 5+ years of software engineering experience in production environments, with strong frontend skills in TypeScript/JavaScript and React, and backend experience in TypeScript or Python focusing on API design. Exposure to working directly with AI/ML technologies and the ability to drive features from concept to production with minimal supervision are essential.

Skills: typescript, javascript, react, python, api design, service architecture, ai/ml, frontend, backend, inference api, ui/ux implementation, monitoring, versioning, observability, evaluation

Summary: Candidates must have over 5 years of software engineering experience, exposure to AI/ML technologies, and strong skills in both frontend development using TypeScript/JavaScript and React, and backend development using TypeScript or Python focusing on API design. A high degree of ownership and the ability to drive features independently are essential.

Skills: typescript, javascript, react, python, api design, service architecture, ai/ml, inference api, frontend, backend, monitoring, versioning, observability, evaluation, data-oriented interfaces, vector databases

Summary: Candidates must have 3+ years of experience delivering AI-first capabilities in customer-facing products, demonstrating strong Python skills and familiarity with front-end technologies like TypeScript and React. Essential expertise includes prompt engineering with top-tier LLMs (OpenAI, Anthropic Claude, Google Gemini) and experience with LLM evaluation frameworks and LLMOps.

Skills: python, llms, typescript, react, webgl, prompt engineering, generative ai, llmops, ci/cd, observability, ml fundamentals, cloud services, vector databases, rag pipelines, langchain, pytorch

Summary: Candidates need a degree in a relevant field and significant professional experience, including at least one year in Generative AI technologies or relevant certifications. Essential requirements include strong problem-solving skills, proficiency in Python and SQL, experience shipping and operating LLM-enabled features in production, and familiarity with modern data/analytics platforms.

Skills: generative ai, agentic workflows, llms, telemetry, observability, data ingestion, data transformation, retrieval strategies, prompt engineering, model evaluation, code reviews, architecture discussions, incident response, data privacy, responsible ai, python

Summary: Candidates must have a degree in a relevant technical field and significant professional experience, including at least one year in Generative AI technologies, or relevant certifications. Essential requirements include strong problem-solving skills, proficiency in Python and SQL, experience shipping and operating LLM-enabled features in production, and familiarity with modern data/analytics platforms.

Skills: generative ai, agentic workflows, llms, telemetry, observability, data ingestion, data transformation, retrieval strategies, prompt engineering, model evaluation, code reviews, architecture discussions, incident response, data privacy, responsible ai, python

Summary: Candidates need a degree in a relevant field and significant professional experience, including at least one year in Generative AI technologies or relevant certifications. Essential requirements include strong problem-solving skills, proficiency in Python and SQL, experience shipping and operating LLM-enabled features in production, and familiarity with modern data/analytics platforms.

Skills: generative ai, agentic workflows, llms, telemetry, observability, data ingestion, data transformation, retrieval strategies, prompt engineering, model evaluation, code reviews, architecture discussions, incident response, data privacy, responsible ai, python

Summary: Candidates must have a degree in a relevant field and significant professional experience, including at least one year in Generative AI technologies, or relevant certifications. Essential qualifications include strong proficiency in Python and SQL, experience shipping and operating LLM-enabled features in production, and strong analytical and communication skills.

Skills: generative ai, agentic workflows, llms, tensorflow, pytorch, python, r, sql, data processing, algorithm development, machine learning models, experiment design, telemetry, observability, data engineering, problem solving

Summary: Candidates need a degree in a relevant field and significant professional experience, including at least one year in Generative AI technologies or relevant certifications. Essential requirements include strong problem-solving skills, proficiency in Python and SQL, experience shipping and operating LLM-enabled features, and familiarity with modern data/analytics platforms.

Skills: generative ai, agentic workflows, llms, telemetry, observability, data ingestion, data transformation, retrieval strategies, prompt engineering, model evaluation, code reviews, architecture discussions, incident response, data privacy, responsible ai, python

Summary: Candidates need a degree in a relevant field and significant professional experience, including at least 1 year in Generative AI technologies, or relevant certifications. Essential requirements include strong Python and SQL skills, experience shipping and operating LLM-enabled features, data-intensive system building, and strong problem-solving abilities.

Skills: generative ai, agentic workflows, llms, tensorflow, pytorch, python, r, sql, data processing workflows, machine learning models, algorithm development, data ingestion, data transformation, retrieval strategies, experiment design, code reviews

Summary: YC-funded startup with meaningful ownership; remote-first and high-growth environment; chance to shape foundational AI/ML systems in benefits at scale.

Skills: python, node.js, openai, anthropic, google ai studio, langchain, langgraph, llamaindex, aws, mysql, pinecone, mlflow, langsmith, langfuse

Summary: Candidates need over 5 years of experience in Conversational AI or IVR development, with strong Java skills including VXML and backend integration experience being essential. Familiarity with contact center platforms like Genesys and tools such as JIRA is also required.

Skills: conversational ai, ivr development, java, vxml, backend integration, genesys, jira, cyara, confluence, rest apis, microservices, aws, sql, nosql, kafka, git

Summary: Candidates must have 3 to 7+ years of experience in Revenue Operations, GTM Systems, or similar roles focused on improving GTM execution through systems and automation, demonstrating a pattern of solving ambiguous problems quickly. Required technical comfort includes APIs, webhooks, JSON payloads, SQL, and strong Salesforce fluency.

Skills: ai agents, automation, data flow, relevance ai, momentum, clay, llm tooling, gtm ai strategy, salesforce fluency, apis, webhooks, json payloads, sql, revenue operations, gtm systems, operational rigor

Summary: Candidates must possess strong, production-quality Python engineering skills, including experience with testing and debugging. Essential applied machine learning experience, particularly in NLP or document AI, is required, alongside hands-on experience integrating LLMs.

Skills: python, machine learning, natural language processing, document ai, llm integration, prompting, structured output, tool calling, function calling, retrieval, ocr, layout parsing, entity extraction, cloud infrastructure, docker, aws

Summary: Candidates typically need 8+ years in Software/ML Engineering/Data Science, with at least 3 years hands-on in Applied AI/LLMs and 2+ years in a senior/lead capacity, requiring deep expertise in Python/TypeScript and distributed cloud systems. Proven success in architecting and shipping complex AI systems at scale, along with strong stakeholder management skills, is essential.

Skills: generative ai, agentic ai, strategy, architecture, foundation models, rag, multi-agent systems, llms, python, typescript, javascript, cloud-native systems, kubernetes, ci/cd, prompt design, model evaluation

Summary: Candidates must have experience building and deploying document extraction or understanding systems that achieved measurable accuracy improvements in production, alongside designing robust evaluation frameworks and datasets. Essential experience includes delivering scalable, observable data processing systems and taking end-to-end ownership of complex AI features from experimentation through to monitoring.

Skills: document extraction, data transformation, large language models, document understanding, evaluation-driven design, production-grade infrastructure, data cleansing, pipeline development, system scalability, observability, model evaluation, experimentation, solution design, ai engineering, underwriting, insurance

Summary: Candidates must have 3+ years of hands-on software engineering experience building and deploying production systems, requiring strong Python skills plus proficiency in Go, TypeScript, or Rust. Essential requirements include hands-on experience with LLMs, RAG systems, agent frameworks, Docker in isolated environments, and strong communication skills for embedded customer engagement.

Skills: python, go, typescript, rust, llms, prompt engineering, rag systems, langchain, langgraph, pydantic-ai, vector databases, docker, kubernetes, apis, data pipelines, linux

Summary: Candidates must have 3+ years of hands-on software engineering experience building production systems, strong Python skills, and experience with LLMs, RAG systems, and agent frameworks. Eligibility and ability to attain the highest level of national and NATO security clearances are mandatory.

Skills: python, go, typescript, rust, llms, prompt engineering, rag systems, langchain, langgraph, docker, kubernetes, apis, data pipelines, linux, communication, customer engagement

Summary: Candidates must have 3+ years of experience building and deploying production software systems, strong Python skills plus Go, TypeScript, or Rust, and hands-on experience with LLMs, RAG systems, and containerization in secure environments. Eligibility for the highest level of national and NATO security clearances is mandatory.

Skills: python, go, typescript, rust, llms, prompt engineering, rag systems, langchain, langgraph, pydantic-ai, vector databases, docker, kubernetes, apis, data pipelines, linux

Summary: Candidates must have 7+ years of professional frontend development experience, deep expertise in React JS and TypeScript, and strong experience building data-dense or visualization-heavy web applications, including map-based UIs with layered GIS data. Experience with Canvas, Web Workers, or WebGL-based rendering techniques is required.

Skills: frontend development, geospatial data, computer vision, react js, typescript, data visualization, gis, map-based uis, canvas, web workers, webgl, performance optimization, api integration, product minded, saas applications, ai-assisted development

Summary: Candidates must have at least two years of experience in software engineering and/or applying machine learning in production, including hands-on experience with LLM applications, prompt engineering, and evaluation frameworks. Required technical expertise includes familiarity with vector or graph databases, document chunking, embedding models, and model evaluation metrics.

Skills: llm, rag systems, vector search, hybrid retrieval, reranking, contextual compression, agent systems, information extraction, information storage, information retrieval, mlops, prompt engineering, model evaluation, graph databases, embedding models, ci/cd

Summary: Candidates must possess an MS degree in Computer Science, AI, Machine Learning, or a related field, along with 3+ years of experience in AI and software development in a commercial setting. Required technical skills include proficiency in languages like Python and experience with frameworks such as PyTorch and TensorFlow.

Skills: ai solutions engineering, machine learning implementation, generative ai, statistical techniques, python, java, r, pytorch, tensorflow, cloud platforms, aws, google cloud, devops tooling, technical communication, deep learning, natural language processing

Summary: Candidates must be authorized to work in the US and possess a minimum of 3 years of experience in AI/ML development, data science, or software engineering, with proficiency in Python or Java. Familiarity with data engineering tools like Hadoop/Spark, cloud services (AWS/Azure), machine learning frameworks (TensorFlow/PyTorch), and SQL is required.

Skills: ai development, machine learning, data collection, data analysis, etl pipelines, optimization, automation, model testing, validation, collaboration, python, java, tensorflow, pytorch, sql, problem-solving

Summary: Candidates need a degree in a relevant field and significant professional experience (8+ years with Bachelor's or 6+ years with Graduate degree), including at least 1 year in Generative AI technologies. Essential qualifications include strong problem-solving skills, experience shipping and operating LLM-enabled features in production, and proficiency in Python and SQL.

Skills: generative ai, large language models, llms, ai workflows, architecture, deployment, monitoring, python, sql, data engineering, apis, data transformation, prompt engineering, evaluation, observability, typescript

Summary: Candidates should possess 5+ years of engineering experience with hands-on development in AI/ML and GenAI solutions, requiring strong proficiency in Python and/or .NET. Demonstrated experience developing LLM-based systems, familiarity with cloud native platforms like Azure and IAC tools, and knowledge of MLOps/LLMOps concepts are essential.

Skills: python, .net, ai/ml, genai, llm-based systems, prompt engineering, orchestration, evaluation, cloud native engineering, azure, iac tools, mlops, llmops, model lifecycle management, technical communication, platform design

Summary: Candidates must possess strong software engineering skills with a proven track record of delivering production software systems, coupled with practical AI experience focusing on real-world solutions. Essential requirements include experience with LLMs, prompt engineering, building GenAI applications, and exposure to cloud-native environments and observability tools.

Skills: ai solutions development, rapid experimentation, cross-functional collaboration, llms, prompt engineering, genai applications, production software systems, cloud-native environments, observability tools, agent frameworks, infrastructure tooling, model fine-tuning, software engineering, incident response, automation

Summary: Candidates must possess strong software engineering skills with experience building production backend or full-stack systems, coupled with practical AI experience focused on real-world solutions. Essential requirements include experience with LLMs, prompt engineering, GenAI applications, production delivery track record, cloud-native exposure, and familiarity with observability tools.

Skills: software engineering, ai, ml, llms, prompt engineering, genai, backend development, full stack development, experimentation, cloud-native environments, observability tools, agent frameworks, kubernetes, docker, terraform, model fine-tuning

Summary: Candidates must have 8+ years of software engineering experience, including 3+ years in AI/LLM systems, with proven hands-on experience building and deploying Agentic AI systems in production environments. Deep expertise in LangChain, LangGraph, AWS AI ecosystem (Bedrock, SageMaker), and building RAG systems with vector databases is required.

Skills: agentic ai architecture, langchain, langgraph, rag, aws ai services, bedrock, sagemaker, vector databases, python engineering, ci/cd, mlops, api development, cloud-native architectures, prompt engineering, llm orchestration, technical leadership

Summary: Strong expertise in applied computer vision and hands-on production experience is required. Proficiency in Python, PyTorch or TensorFlow, and solid experience with detection and tracking models are essential.

Skills: applied computer vision, python, software engineering, pytorch, tensorflow, detection models, tracking models, mlops, technical leadership, communication skills, startup environment, team building

Summary: Candidates should have a Bachelor’s degree in Computer Science or a related field with 2+ years of experience, or a Master’s degree with relevant experience. Proficiency in programming languages such as TypeScript/JavaScript and Python, along with experience in building full-stack applications, is required.

Skills: software engineering, ai integration, full-stack development, frontend development, backend services, api integration, generative ai, large language models, agent frameworks, testing, debugging, monitoring, collaboration, problem solving, ethics, security

Summary: A Master’s degree in a quantitative field and at least 4 years of experience in machine learning and statistics are required. Candidates should also be comfortable using Linux and have the ability to obtain a Top Secret clearance.

Skills: machine learning, statistics, data science, signal processing, cloud infrastructure, python, sql, data pipelines, etl processes, model deployment, linux, ml governance, tensorflow, pytorch, scikit-learn, experiment tracking, reproducible research

Summary: Candidates should have strong Python development experience and familiarity with AI/LLM services. Experience in leading technical teams and working with cloud platforms is also important.

Skills: python development, ai integration, llm services, agent frameworks, technical communication, rapid prototyping, cloud platforms, data extraction, target identification, collaboration, automation, orchestration architecture, machine learning, research, interdisciplinary teams, problem solving

Summary: Candidates must possess deep hands-on experience with React, Next.js, and Node.js, coupled with strong fundamentals in building production-ready, high-quality systems, including cloud infrastructure knowledge (AWS preferable). A key requirement is practical experience applying AI responsibly in real delivery contexts and the ability to guide clients on realistic AI opportunities.

Skills: full stack development, typescript, react, next.js, node.js, javascript ecosystem, cloud infrastructure, aws, ci/cd pipelines, devops mindset, ai integration, technical leadership, consulting, client delivery, performance optimization, accessibility

Summary: Candidates must have a minimum of 7 years of experience in software development and AI engineering, with expertise in Python and AWS. Hands-on experience with Generative AI integration and building API services is critical.

Skills: python, aws, generative ai, api development, microservices, devops, ci/cd, ml pipelines, rag, agentic ai, langchain, crewai, semantic kernel, performance optimization, error handling, evaluation frameworks

Summary: Candidates must have 7+ years of backend, data, or infrastructure engineering experience, demonstrating expertise in distributed systems, big data infrastructure, and applied AI/ML, including LLMs. Proficiency in languages like Java, Go, or Python, along with strong SQL skills and familiarity with tools like Kafka and ClickHouse, is required.

Skills: distributed systems engineering, high-throughput apis, scalable microservices, event processing pipelines, big data infrastructure, applied ai/ml, large language models (llms), java, go, python, sql, data modeling, kafka, kinesis, clickhouse, llm-powered systems

Summary: A Master’s degree in a quantitative field and at least 4 years of experience are required, along with a strong background in machine learning, mathematics, and statistics. Candidates must be comfortable using Linux and have the ability to obtain a Top Secret clearance.

Skills: machine learning, statistics, data science, signal processing, cloud computing, python, sql, data engineering, etl processes, model deployment, linux, governance practices, experiment tracking, model monitoring, reproducible research, information theory, adversarial ai

Summary: The ideal candidate is a senior full-stack engineer comfortable across the entire stack who possesses genuine curiosity about AI and is excited to apply it creatively to real-world problems. They must be product-minded, self-directed, thrive with autonomy, and be a clear communicator within a fast-moving team.

Skills: full stack engineering, ai integration, backend services, frontend interfaces, autonomous ai systems, platform scaling, react, node.js, postgres, aws, product ownership, technical direction, user feedback iteration, vercel, inngest, twilio/whatsapp

Summary: Candidates should have proven experience in building and operating production AI systems and implementing the full lifecycle of early-phase AI development. Familiarity with tools like PyTorch and TensorFlow, as well as a strong understanding of connecting modeling tasks with business value, is essential.

Skills: ai systems, machine learning, deep learning, data analysis, prototyping, model deployment, healthcare data, generative ai, ranking, categorization, search, collaboration, mentorship, software engineering, business objectives, performance measurement

Summary: Candidates should have proven experience in building and operating production AI systems and implementing the full lifecycle of AI development. Familiarity with tools like PyTorch and TensorFlow, as well as a strong understanding of business value in AI, is essential.

Skills: ai development, outcome-driven engineering, ai system design, mentorship, data science, machine learning, deep learning, pytorch, tensorflow, generative ai, aws, azure ml, databricks, healthcare, model evaluation, collaboration

Summary: Candidates should have proven experience building and operating production AI systems and implementing the full lifecycle of AI development. Familiarity with tools like PyTorch and TensorFlow, as well as a relevant advanced degree, is advantageous.

Skills: ai systems, unstructured data, model implementation, ai performance, collaboration, prototyping, deep learning, pytorch, tensorflow, hugging face, databricks, aws bedrock, azure ml

Summary: Candidates should have experience with AI/ML systems in production, familiarity with prototyping AI solutions, and an understanding of the AI development lifecycle. Hands-on experience with tools like PyTorch and TensorFlow is preferred, along with the ability to connect technical work with business value.

Skills: ai, machine learning, healthcare, data management, model implementation, collaboration, pytorch, tensorflow, generative ai, monitoring, evaluation, stakeholder engagement, prototyping, deployment, business value, technical expertise

Summary: Candidates must be enrolled full-time in an undergraduate or graduate degree program and have coursework or project experience in AI/ML or related fields. Familiarity with programming languages and interest in data engineering and cloud computing is also required.

Skills: ai, ml, data science, algorithms, python, java, c++, data engineering, analytics, cloud computing, aws, azure, gcp, docker, kubernetes, llms, prompt engineering

Summary: The ideal candidate should have at least 2 years of experience specifically training Large Language Models and possess deep technical knowledge of modern deep learning architectures. Proven proficiency in PyTorch or JAX with a track record of deploying robust ML systems into production is also required.

Skills: machine learning, deep learning, pytorch, jax, optimization techniques, model deployment, large language models, research application

Summary: Candidates should be proficient in Python and have hands-on experience with LLMs and GCP. Familiarity with DevOps practices and document handling is also required.

Skills: python, ai development, gcp, llms, rest apis, devops, docker, postgres, redis, azure blobs, a/b testing, ci/cd, observability, data handling, text normalisation, chunking strategies

Summary: Candidates should have experience in machine learning, natural language processing, and Azure AI services. Proficiency in programming languages such as Python, C#, and JavaScript is also required.

Skills: machine learning, ai model development, natural language processing, text analytics, unstructured data processing, azure ai services, python development, vector databases, semantic search, large language model integration, data indexing, content classification, c# development, javascript, rest api development, sharepoint ai integration

Summary: Candidates must possess a Bachelor’s or Master’s degree in a quantitative field and have 2 to 5 years of experience in ML engineering or related software development with an AI/ML focus. Essential technical requirements include strong Python skills, proficiency with ML frameworks, hands-on experience deploying LLMs, and solid experience with cloud platforms and container orchestration tools like Docker and Kubernetes.

Skills: ai/ml solutions development, ml pipeline development, llm integration, generative ai, model optimization, cloud platforms (aws, gcp, azure), containerization (docker, kubernetes), mlops practices, ci/cd, python programming, tensorflow, pytorch, scikit-learn, data engineering principles, kubeflow orchestration, model monitoring

Summary: Candidates should have solid professional experience in software engineering, ideally 5+ years, with strong coding skills in C#, JavaScript, TypeScript, React, and Node. Familiarity with AI application patterns and tools, as well as cloud services, is also required.

Skills: c#, javascript, typescript, react, node, ai tools, llms, rag pipelines, vector search, ai-driven analytics, problem-solving, communication, collaboration, cloud services, ai code generation, mentorship

Summary: Candidates should have a degree in Computer Science, AI, Machine Learning, or a related field, along with 5+ years of experience in developing AI/ML solutions. Expertise in Python and agent frameworks is essential, along with strong collaboration and mentorship skills.

Skills: python, ai, machine learning, autonomous agents, agent frameworks, generative ai models, fastapi, asyncio, pytorch, distributed systems, cloud-native architectures, collaboration, mentorship, communication