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 engineering experience with a strong recent track record building production AI / LLM applications
• Strong agent-design judgment to match task harness, context, failures, and policies
• Ability to lead discovery and feasibility conversations with SMEs and end-users and explain technical trade-offs
• Hands-on experience with agentic frameworks such as LangChain, LangGraph, Semantic Kernel and major LLM providers like OpenAI, Anthropic, Google Gemini
• Expert-level Python programming and solid software engineering fundamentals
• Strong RAG and retrieval skills including vector databases, embeddings, hybrid search, re-ranking, chunking, and context management
• Experience evaluating generative AI quality using LLM-based evaluation, heuristics, custom eval frameworks, and observability/tracing tools like LangSmith
• Production deployment on major cloud providers (AWS, Azure, or GCP) with containerization and CI/CD
• Sound judgment under ambiguity, balancing speed, quality, and scope
• English language proficiency at C1 level

Nice to have:

• Experience 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 production experience
• Familiarity with MCP, A2A, Agent Skills, and emerging agent standards
• Experience with enterprise AI platforms such as AWS Bedrock AgentCore, Databricks Genie, Microsoft Foundry, Gemini Enterprise
• Exposure to AI governance, security, and compliance topics including guardrails and prompt-injection prevention
• Prior client-facing or pre-sales experience in consulting or services context

• Design, build, and ship AI-native systems end-to-end, including agents, workflows, RAG, custom tool calling, sandboxing, context engineering, sub-agents, caching, and compaction
• Build evaluation pipelines to prove system usefulness
• Design for failure in the agent loop, including retries, model fallbacks, cost limits, and human-in-the-loop on consequential actions
• Capture domain expertise and repeatable workflows for reuse across engagements
• Engage early in use case shaping and technical feasibility
• Write production-grade Python code for integrations, APIs, data access, and deployment
• Work directly with SMEs and end-users through interviews, UAT, and workflow observation to validate system fit

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