Chief Forward Deployed Engineer

EPAM builds AI-native solutions for our clients — products where LLM and its harness are the core of the value. This is a builder's role: you and your team are responsible for building agentic systems, writing the production code, and standing up the evals and observability. You work closely with SMEs and end-users to understand where the real value lies, and you design the feedback loops.

• Design, build, and ship AI-native systems E2E — agents, workflows, RAG, and the harness: custom tool calling, sandboxing, context engineering and sub-agents, caching, compaction
• Build the evaluation pipelines and use them to prove the system is genuinely useful
• Design for failure in the agent loop: retries, model fallbacks, cost limits, and human-in-the-loop on consequential actions
• Capture domain expertise and repeatable workflows — so what works on one engagement carries to the next
• Engage early, to help shape the use case and check technical feasibility
• Write production-grade Python: integrations, APIs, data access, deployment
• Work directly with SMEs and end-users — interviews, UAT, observing the real workflow — and validate that the system fits how people actually work

• 7+ years of engineering experience, with a strong recent track record building production AI / LLM applications (not prototypes or research only)
• Strong agent-design judgment — task-harness fit, matching the harness to the context, failures, and policies of the actual task rather than calling a model in a loop The ability to operate close to the client: lead discovery and feasibility conversations, work directly with SMEs and end-users, and explain technical trade-offs to both technical and non-technical audiences
• Hands-on experience with agentic frameworks (Lang Chain, Lang Graph, Semantic Kernel, or similar) and major LLM providers (OpenAI, Anthropic, Google Gemini)
• Expert-level Python and solid software engineering fundamentals
• Strong RAG and retrieval skills: vector databases, embeddings, hybrid search, re-ranking, chunking, and context management
• Proven experience evaluating generative AI quality — LLM-based evaluation, heuristics, custom eval frameworks — and using observability/tracing tools (Lang Smith, Arize Phoenix, Langfuse, or similar)
• Production deployment experience on at least one major cloud (AWS, Azure, or GCP) with containerization, CI/CD
• Sound judgment under ambiguity — scoping, sequencing, and making the call on speed vs. quality vs. scope
• English at C1 level
• Nice to have Experience designing experiments, A/B testing, and iterating on AI products against real user behavior and business metrics
• Background in NLP, Data Science, or applied ML, with experience moving models into production
• Familiarity with MCP, A2A, Agent Skills, and emerging agent standards
• Experience with enterprise AI platforms (AWS Bedrock Agent Core, Databricks Genie, Microsoft Foundry, Gemini Enterprise) Exposure to AI governance, security, and compliance (guardrails, prompt-injection prevention) Prior client-facing or pre-sales exposure in a consulting or services context