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)
- 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
