Chief Forward Deployed Engineer

EPAM is a 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, EPAM empowers clients to become AI-Native enterprises, driving measurable value from innovation and digital investments.

• 7+ years of engineering experience with recent production AI/LLM application development
• Strong agent design judgment considering harness-task fit, failure policies, and task context
• Ability to lead discovery, feasibility conversations, and communicate technical trade-offs effectively to technical and non-technical stakeholders
• Hands-on experience with agentic frameworks such as LangChain, LangGraph, Semantic Kernel, and major LLM providers like OpenAI, Anthropic, or Google Gemini
• Expert-level Python and solid software engineering fundamentals
• Experience with RAG methodologies, including vector databases, embeddings, hybrid search, and context management
• Experience with generative AI evaluation techniques, heuristics, custom eval frameworks, and observability tools like LangSmith, Arize Phoenix, or Langfuse
• Production deployment experience on major clouds (AWS, Azure, GCP) with containerization and CI/CD
• Sound judgment to balance speed, quality, and scope under ambiguity
• English language proficiency at C1 level

Nice to have:

• Experience designing experiments, A/B testing, and iterating on AI products based on user behavior and business metrics
• Background in NLP, Data Science, or applied ML with production model deployment 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, or 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, RAG, and harness components like custom tool calling and context engineering
• Build evaluation pipelines to demonstrate system usefulness
• Design for failure in agent loops including retries, model fallbacks, cost limits, and human-in-the-loop on critical actions
• Capture domain expertise and repeatable workflows for reuse across engagements
• Engage early in use case shaping and technical feasibility assessment
• 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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