Ai Engineering Jobs with Salary ≥ 80k

Summary: Candidates should have over 5 years of experience in writing production-level code in Python and at least 2 years in deploying conversational AI models. A strong understanding of machine learning methods and experience with deep learning frameworks is also required.

Skills: python, conversational ai, aiops, llmops, machine learning, deep learning, reinforcement learning, nlp, azure, aws, microservices, asynchronous messaging, serverless architecture, azure cognitive services, git, unit testing, debugging

Summary: Candidates must have strong proficiency in Java Spring Boot and hands-on experience with Python for scripting and automation. A solid understanding of AI/ML concepts and experience with cloud-native services on AWS or Azure is essential.

Skills: python, ai/ml, java spring boot, microservices, cloud-native services, aws, azure, restful api development, machine learning, nlp, tensorflow, pytorch, scikit-learn, sql, nosql, devops

Summary: Candidates should have a Bachelor’s or Master’s degree in a relevant field and at least 5 years of experience in developing production-grade AI/ML systems. Strong programming skills in Python and familiarity with AI frameworks and cloud infrastructure are essential.

Skills: ai solution development, collaboration, technical execution, performance optimization, innovation, python, pytorch, tensorflow, hugging face, langchain, openai apis, ml lifecycle, llms, cloud infrastructure, problem-solving, communication

Summary: The role requires ideally 7+ years of software engineering experience, with 3+ years specifically in building ML/AI-powered features in production. Candidates should have a strong background as a Staff-level product software engineer and experience with LLMs and prompt engineering.

Skills: software engineering, ml/ai-powered features, production systems, prompt engineering, model tuning, evaluation strategies, backend development, collaboration, experimentation, llms, retrieval-augmented generation, cross-functional skills, quantitative signals, qualitative signals, user experience, ai features

Summary: The role requires ideally 7+ years of software engineering experience, with 3+ years focused on building ML/AI-powered features in production. Candidates should have a strong background as a Staff-level product software engineer and deep expertise in LLMs and prompt engineering.

Skills: software engineering, machine learning, ai features, prompt engineering, model tuning, evaluation strategies, backend development, python, go, ruby, typescript, collaboration, experimentation, user experience, observability tools, production infrastructure, llms

Summary: Candidates should have over 7 years of experience in web application development and at least 3 years with technologies like C#, ASP.NET Core, and Angular. Experience with AI/ML models and a strong understanding of OOP concepts and design patterns is also required.

Skills: software development, ai integration, machine learning, web applications, c#, asp.net core, angular, typescript, restful apis, cloud infrastructure, devops, oop concepts, design patterns, agile environment, database management, code reviews

Summary: Candidates should have 4+ years of experience in software engineering, ML engineering, or data science, with strong programming skills in Python. Familiarity with AI/ML infrastructure and a willingness to work with frontend technologies are also required.

Skills: python, docker, postgres, redis, typescript, react, ai, ml, data science, legal engineering, large language models, analytical systems, context engineering, memory in ai, unstructured data, quantitative skills

Summary: The ideal candidate should have over 5 years of experience in building scalable backend systems, with expert-level proficiency in Python and strong experience in API design. Familiarity with PostgreSQL, vector databases, and Docker is essential, along with a forward-thinking mindset for integrating AI-generated code.

Skills: python, grpc, postgresql, vector databases, docker, large language models, api design, orm, concurrency, architectural guidance, data pipelines, ai integration, scalable systems, vibe coding, rapid iteration, expert review

Summary: Candidates should have a Bachelor's degree in Computer Science, Artificial Intelligence, or a related field, with a strong focus on machine learning. Additionally, 10+ years of experience in engineering or technology leadership and 5+ years in a leadership role managing technical teams are required.

Skills: ai strategy, team leadership, data integration, model deployment, ai lifecycle management, technical guidance, project management, operational frameworks, ai best practices, collaboration, mentorship, innovation, ai technologies, cloud platforms, machine learning, data engineering

Summary: Candidates should have proven experience in technical consulting or solutions architecture, with deep expertise in OpenShift or Kubernetes. Familiarity with inference technologies and functional Python skills are also required.

Skills: technical consulting, solutions architecture, openshift, kubernetes, system performance, inference technologies, python, stakeholder communication, optimization, resource management, field enablement, product feedback, networking knowledge, model tuning, performance optimization, ai inference solutions

Summary: Senior Team Lead for AI engineering; drive online mentoring, project reviews, stand-ups, and virtual classrooms; requires 2-3+ years in data/engineering roles; top skills: python, pytorch, ai engineering.

Skills: python, pytorch, ai engineering

Summary: Candidates must have an MS/Ph.D. in a relevant field and at least 2 years of experience in AI engineering leadership roles. Strong technical expertise in modern NLP and coding in Python is required.

Skills: ai engineering, conversational ai, large language models, nlp, data curation, knowledge base construction, llmops, python, sql, vector databases, knowledge graphs, communication skills, adaptability, team management, research, product management

Summary: You should have experience making technology decisions and implementing projects with multiple programming languages. Strong communication skills in both German and English, along with analytical and solution-oriented thinking, are essential.

Skills: ai engineering, software development, data modeling, api development, backend services, retrieval-augmented generation, agentic ai, tool orchestration, postgresql, mongodb, docker, kubernetes, tensorflow, pytorch, langchain, langgraph

Summary: You should have experience making technology decisions and implementing projects with multiple programming languages. Strong communication skills in both German and English, along with analytical and solution-oriented thinking, are essential.

Skills: ai engineering, software development, data modeling, api development, backend services, rag architectures, agentic ai, tool orchestration, postgresql, python, go, typescript, docker, kubernetes, tensorflow, pytorch

Summary: A Bachelor's degree is required, with a Master's preferred. Candidates should have at least 2 years of experience in machine learning, AI model development, or data science.

Skills: ai engineering, machine learning, generative ai, data analysis, sql, python, spark, data visualization, exploratory data analysis, cloud architecture, data governance, nlp, model validation, etl workflows, apis, statistical analysis

Summary: Candidates should have 2-5 years of experience in software development or AI tooling, along with a bachelor's degree in a related field. Strong knowledge of AI application development and proficiency in Python or similar backend frameworks is essential.

Skills: ai engineering, langchain, langflow, azure ai studio, openai apis, hugging face, semantic search, prompt engineering, python, api integration, intelligent automation, cross-functional collaboration, enterprise resource planning, customer information system, vector-based retrieval, agent chaining

Summary: Lead AI-powered reliability features using LLMs and agents; backend scalability, retrieval-augmented search, and AI evaluation. Focus on fast iteration, ownership, and impact. Top tech: tailwind, LLMs, agent frameworks.

Skills: tailwind

Summary: The ideal candidate has shipped LLM and generative AI-based applications to production and is proficient in Python and building APIs. They should keep up to date with the latest AI developments and thrive on intellectual challenges.

Skills: ai engineering, machine learning, llm, python, api development, cloud computing, graph data, generative ai, problem solving, collaboration, product development, research engineering, data analysis, software development, innovation, intellectual challenge

Summary: Candidates must have a Bachelor's degree in a STEM field and at least 10 years of relevant experience, or a Master's degree with a minimum of 8 years. Deep learning expertise and leadership experience are essential, along with the ability to obtain a DoD Top Secret security clearance.

Skills: artificial intelligence, deep learning, data visualization, software development, machine learning, python, git, docker, kubernetes, linux, nlp, signal data, aws, azure, cyber compliance, technical leadership

Summary: Candidates should have over 10 years of experience in AI, software engineering, or data science, with at least 3-5 years in leadership roles. A Master's or Ph.D. in a relevant field is required, along with hands-on expertise in cloud environments.

Skills: ai, machine learning, generative ai, predictive modeling, python, mlops, cloud platforms, aws, azure, gcp, engineering leadership, software design, api development, customer engagement, business strategy, technical architecture, team development

Summary: Candidates should have a bachelor's degree in a relevant field and 2-4 years of experience in machine learning or AI roles. Strong technical skills in various ML frameworks and data engineering are essential, along with good analytical and communication abilities.

Skills: machine learning, ai, mlops, nlp, generative ai, llms, tensorflow, pytorch, scikit-learn, spark, data engineering, etl pipelines, sql, nosql, data preprocessing, analytical skills

Summary: Hands-on experience with LLM frameworks and APIs is required, along with 5+ years of software engineering experience. Familiarity with cloud-hosted environments and a strong knowledge of back-end technologies is essential.

Skills: generative ai, llm frameworks, apis, python, javascript, typescript, react, node.js, aws, nlp, machine learning, agile, problem-solving, communication, documentation, mentoring

Summary: Candidates should have an M.S. in Computer Science or equivalent experience and at least 5 years of experience as a Research or Machine Learning Engineer. Expertise in Python and experience with deploying and operating Deep Learning models using PyTorch are essential.

Skills: python, mlops, llmops, deep learning, pytorch, transformers, natural language processing, computer vision, knowledge representation, generative ai, containers, kubernetes, aws, databricks, vector databases, agentic infrastructure

Summary: A Bachelor’s degree in Computer Science, Data Science, or a related field is required, along with 8 or more years of applicable professional experience in Machine Learning or AI Engineering. Hands-on expertise in developing and deploying AI/ML models in production environments is preferred.

Skills: artificial intelligence, machine learning, mlops, predictive analytics, generative ai, optimization, collaboration, technical standards, performance management, mentoring, operational efficiency, forecast accuracy, risk reduction, azure ai

Summary: Candidates should have over 10 years of software development experience with a focus on AI/ML projects. A Bachelor's or Master's degree in Computer Science, AI/ML, or a related field is required, along with hands-on experience in developing AI agents.

Skills: software development, ai/ml projects, ai agents, google adk, langchain, crewai, autogen, programming skills, llm integration, prompt engineering, multi-agent workflows, vector databases, embeddings, generative ai, nlp, reinforcement learning

Summary: A minimum of 4 years of hands-on experience in AI/ML development is required, along with proficiency in Python and modern ML libraries. Familiarity with cloud platforms and experience with MLOps pipelines are also preferred.

Skills: ai engineering, machine learning, python, api development, data pipelines, observability, collaboration, cloud platforms, mlops, generative ai, prompt engineering, fastapi, flask, containerization, vector databases, agent frameworks

Summary: Candidates should have 8+ years of experience in Python and 3+ years in deploying high-volume AI/ML systems. Deep expertise in AI inference services and a strong understanding of the conversational AI lifecycle is essential.

Skills: python, conversational ai, machine learning, deep learning, natural language processing, reinforcement learning, azure, aws, microservices, asynchronous messaging, git, unit testing, debugging, profiling, cloud architecture, data storage

Summary: Candidates should have 10-15+ years in software engineering with significant leadership experience in AI/ML systems. A proven history of shipping LLM-powered features and expertise in backend systems and cloud infrastructure is essential.

Skills: ai systems strategy, machine learning, technical leadership, product development, ai engineering, cloud infrastructure, distributed systems, llm orchestration, observability, experimentation, team building, personalization, natural language processing, data science, automation, user experience

Summary: Candidates should have 3-7 years of experience in machine learning engineering or applied research with strong software engineering fundamentals. A deep understanding of modern ML techniques, particularly in computer vision and NLP, is essential.

Skills: machine learning, document understanding, computer vision, natural language processing, data pipeline development, model training, ml infrastructure, technical communication, open-source collaboration, vision-language models, transformer architectures, model fine-tuning, evaluation frameworks, mlops tools, docker, kubernetes