Chief Forward Deployed Engineer

EPAM is global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and ambitious startups. With over thirty years of expertise in custom software, product and platform engineering, we empower our clients to become AI-Native enterprises, driving measurable value from innovation and digital investments.

• 7+ years of engineering experience with a strong recent track record building production AI / LLM applications
• Expertise in agent design, including task-harness fit and policy management beyond simple model loops
• Ability to work closely with clients, lead discovery and feasibility discussions, and communicate technical trade-offs to technical and non-technical audiences
• Hands-on experience with agentic frameworks such as LangChain, LangGraph, Semantic Kernel, and major LLM providers including OpenAI, Anthropic, Google Gemini
• Expert-level Python proficiency and strong software engineering fundamentals
• Strong skills in Retrieval-Augmented Generation (RAG) and retrieval techniques including vector databases, embeddings, hybrid search, re-ranking, chunking, and context management
• Experience evaluating generative AI quality with LLM-based evaluations, heuristics, custom evaluation frameworks, and using observability/tracing tools like LangSmith, Arize Phoenix, Langfuse
• Production deployment experience on major cloud platforms (AWS, Azure, or GCP) with containerization and CI/CD
• Sound judgment in ambiguous situations involving scoping, sequencing, and trade-offs between speed, quality, and scope
• English proficiency at C1 level

Nice to have:

• Experience in designing experiments, A/B testing, and iterating AI products based on user behavior and business metrics
• Background in NLP, Data Science, or applied ML with experience moving models into production
• Familiarity with agent standards such as MCP, A2A, Agent Skills
• Experience with enterprise AI platforms like AWS Bedrock AgentCore, Databricks Genie, Microsoft Foundry, Gemini Enterprise
• Exposure to AI governance, security, and compliance including guardrails and prompt-injection prevention
• Prior client-facing or pre-sales experience in consulting or services contexts

• Design, build, and ship AI-native systems end-to-end including agents, workflows, Retrieval-Augmented Generation (RAG), and associated components such as custom tool calling, sandboxing, context engineering, sub-agents, caching, and compaction
• Build evaluation pipelines to prove the system's usefulness
• Design for failure in agent loops with retries, model fallbacks, cost limits, and human-in-the-loop for consequential actions
• Capture domain expertise and repeatable workflows to apply learnings across engagements
• Engage early to shape use cases and assess technical feasibility
• Write production-grade Python code including integrations, APIs, data access, and deployment
• Work directly with SMEs and end-users through interviews, user acceptance testing (UAT), and workflow observation to validate system fit

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