Senior AI Engineer / Data Scientist

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)