AI Engineer - Agentic AI & Back End

We’re looking for a Senior AI Engineer – Agentic AI & Backend to join our team in London, United Kingdom in a hybrid working mode. This role is part of EPAM’s Data & AI Practice and focuses on building practical AI solutions that move clients from concept to production-ready systems. You will work across sectors such as Financial Services, CPG and Retail, helping design and implement AI-powered workflows and backend services that deliver measurable business outcomes.

This position offers an opportunity to work on innovative AI technologies, backend architectures, and real-world agentic systems in enterprise environments.

Responsibilities

Design and implement AI solutions, focusing on agentic workflows and backend engineering Build backend services and APIs in Python using frameworks such as FastAPI or similar Develop orchestration patterns for agentic workflows, including intent classification, tool calling, routing and supervisor/worker flows Create and maintain AI tools and skills with clear interfaces, testing and secure execution Configure and integrate MCP servers and clients for enterprise-grade agent connectivity Develop RAG-based solutions, including GraphRAG and Agentic RAG, aligned with client requirements Work with agent frameworks and SDKs such as Pydantic AI, Lang.

Graph/Lang. Chain or Microsoft Agent Framework Define evaluation strategies for agentic systems using automated and human-in-the-loop methods Implement telemetry and observability solutions to monitor performance and quality Leverage AI-assisted tools such as Claude Code, OpenAI Codex or GitHub Copilot to accelerate development Requirements Bachelor’s or Master’s degree in Computer Science, Engineering, or related field with 4+ years of hands-on ML/AI engineering experience (or PhD with relevant experience).

Strong Python development skills with proven backend engineering experience Hands-on experience building agentic or LLM-powered applications beyond simple prototypes Knowledge of API design, asynchronous processing, testing, debugging and production reliability Experience with Azure or AWS cloud environments and related services Familiarity with agent workflows, orchestration patterns and tool-calling techniques Experience integrating MCP or building RAG-based solutions with quality evaluation mechanisms Understanding of evaluation methodologies for AI systems such as golden datasets and automated tests Proficiency in telemetry, logging and observability tools for production systems Ability to work in cross-functional client engagement teams delivering enterprise solutions Nice to have Experience using AI development tools such as Claude Code, OpenAI Codex or GitHub Copilot Exposure to Azure AI Search, Azure Application Insights, Amazon Bedrock or other cloud AI services Familiarity with Microsoft 365 Agents SDK, Copilot SDK or enterprise Copilot extensions Knowledge of AI safety practices, guardrails and secure execution for AI workflows Prior consulting or client-facing experience in enterprise AI solution delivery