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Senior AI Engineer / Data Scientist
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingDesign, build, and deploy Generative and Agentic AI solutions from prototype to production
Develop and optimize multi-agent systems using LangGraph, CrewAI, AutoGen, or Semantic Kernel
Design RAG pipelines with embeddings, chunking, hybrid search and retrieval evaluation
Build production-grade Python APIs and microservices that integrate with enterprise systems
Apply observability and monitoring (Langfuse, Arize, Grafana) to keep systems reliable
From the employer’s posting
Design, build, and deploy Generative AI and Agentic AI solutions from prototyping through production
Develop and optimize multi-agent systems using frameworks such as LangGraph, CrewAI, AutoGen, and Semantic Kernel
Design and build RAG pipelines, including embeddings, chunking, hybrid search, and retrieval evaluation for enterprise data grounding
Build production-grade Python APIs and microservices integrating with enterprise systems and AI services
Apply observability and monitoring solutions (Langfuse, Arize, Grafana) to ensure system reliability
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 1, 20262 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Sep 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EPAM Systems is hiring
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- Azure
- Grafana
- MLflow
- Airflow
Where the posting names them
We're looking for a Senior AI Engineer / Data Scientist to join our team in London, United Kingdom, in a hybrid working mode. This role is at the core of EPAM’s Data & AI Practice and focuses on building state-of-the-art Generative AI, Agentic AI, and advanced data science solutions for real-world business problems. You’ll design and develop multi-agent systems, implement RAG pipelines, and deliver production-ready AI applications that transform client capabilities across multiple industries. If you're passionate about pushing the frontiers of LLMs, orchestration frameworks, and scaling AI systems into production, this opportunity offers a high-growth environment and cutting-edge challenges. Responsibilities Design, build, and deploy Generative AI and Agentic AI solutions from prototyping through production Develop and optimize multi-agent systems using frameworks such as LangGraph, CrewAI, AutoGen, and Semantic Kernel Implement orchestration patterns including planner/executor, supervisor/worker, and tool-calling workflows Design and build RAG pipelines, including embeddings, chunking, hybrid search, and retrieval evaluation for enterprise data grounding Develop orchestration engines supporting multi-step planning, delegation, and fallback paths for agent workflows Implement integration and communication patterns via MCP, A2A, OpenAPI, REST, and gRPC Build production-grade Python APIs and microservices integrating with enterprise systems and AI services Apply observability and monitoring solutions (Langfuse, Arize, Grafana) to ensure system reliability Contribute to solution architecture, best engineering practices, and documentation Requirements Bachelor’s/Master’s in Computer Science, Data Science, or related field with 4+ years’ experience, or Ph.D. with relevant experience Strong engineering experience with Python, APIs, microservices, debugging, and code review Proven experience building and deploying Generative AI or Agentic AI applications in production Deep understanding of LLM concepts, RAG patterns, prompt design, and evaluation methodologies Experience with multi-agent orchestration frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel) Familiarity with orchestration strategies like planner/executor and tool calling Knowledge of MCP, A2A protocols, and OpenAPI-based integration methods Strong experience with cloud environments, ideally Azure (Azure OpenAI, AI Foundry, AI Search) Competence in containerized deployments, CI/CD, and MLOps tooling (MLFlow, Airflow) Nice to have Experience with Microsoft Agent Framework, Azure AI Agent Service Knowledge of vector databases (Pinecone, Weaviate, Qdrant, Milvus) Familiarity with guardrail and AI safety techniques (output filtering, prompt injection defense) Experience in distributed systems, event-driven architectures, and workflow engines Prior involvement in training, fine-tuning, or experimenting with foundation models
Your next step
Complete your application on careers.epam.com. The employer’s form will show what is required.
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About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
We're looking for a Senior AI Engineer / Data Scientist to join our team in London, United Kingdom, in a hybrid working mode. This role is at the core of EPAM’s Data & AI Practice and focuses on building state-of-the-art Generative AI, Agentic AI, and advanced data science solutions for real-world business problems. You’ll design and develop multi-agent systems, implement RAG pipelines, and deliver production-ready AI applications that transform client capabilities across multiple industries. If you're passionate about pushing the frontiers of LLMs, orchestration frameworks, and scaling AI systems into production, this opportunity offers a high-growth environment and cutting-edge challenges. Responsibilities Design, build, and deploy Generative AI and Agentic AI solutions from prototyping through production Develop and optimize multi-agent systems using frameworks such as LangGraph, CrewAI, AutoGen, and Semantic Kernel Implement orchestration patterns including planner/executor, supervisor/worker, and tool-calling workflows Design and build RAG pipelines, including embeddings, chunking, hybrid search, and retrieval evaluation for enterprise data grounding Develop orchestration engines supporting multi-step planning, delegation, and fallback paths for agent workflows Implement integration and communication patterns via MCP, A2A, OpenAPI, REST, and gRPC Build production-grade Python APIs and microservices integrating with enterprise systems and AI services Apply observability and monitoring solutions (Langfuse, Arize, Grafana) to ensure system reliability Contribute to solution architecture, best engineering practices, and documentation Requirements Bachelor’s/Master’s in Computer Science, Data Science, or related field with 4+ years’ experience, or Ph.D. with relevant experience Strong engineering experience with Python, APIs, microservices, debugging, and code review Proven experience building and deploying Generative AI or Agentic AI applications in production Deep understanding of LLM concepts, RAG patterns, prompt design, and evaluation methodologies Experience with multi-agent orchestration frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel) Familiarity with orchestration strategies like planner/executor and tool calling Knowledge of MCP, A2A protocols, and OpenAPI-based integration methods Strong experience with cloud environments, ideally Azure (Azure OpenAI, AI Foundry, AI Search) Competence in containerized deployments, CI/CD, and MLOps tooling (MLFlow, Airflow) Nice to have Experience with Microsoft Agent Framework, Azure AI Agent Service Knowledge of vector databases (Pinecone, Weaviate, Qdrant, Milvus) Familiarity with guardrail and AI safety techniques (output filtering, prompt injection defense) Experience in distributed systems, event-driven architectures, and workflow engines Prior involvement in training, fine-tuning, or experimenting with foundation models
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