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, they empower clients to become AI-native enterprises, driving measurable value from innovation and digital investments.

• 7+ years engineering experience with a strong recent record in production AI/LLM applications
• Strong judgment in agent design, including task-harness fit and failure policies
• Ability to lead discovery, feasibility discussions, and communicate technical trade-offs to technical and non-technical audiences
• Hands-on experience with agentic frameworks like LangChain, LangGraph, Semantic Kernel, or similar, and LLM providers such as OpenAI, Anthropic, Google Gemini
• Expert-level proficiency in Python and software engineering fundamentals
• Strong skills in RAG and retrieval: vector databases, embeddings, hybrid search, re-ranking, chunking, context management
• Proven experience in generative AI quality evaluation using LLM-based methods, heuristics, custom eval frameworks, and observability tools like LangSmith, Arize Phoenix, Langfuse
• Production deployment experience on major clouds (AWS, Azure, or GCP) with containerization and CI/CD
• Sound judgment under ambiguity involving scoping, sequencing, and trade-offs
• 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 model deployment experience
• Familiarity with MCP, A2A, Agent Skills, and emerging agent standards
• Experience with enterprise AI platforms (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 context

• Design, build, and ship AI-native systems E2E — agents, workflows, RAG, and the harness including custom tool calling, sandboxing, context engineering and sub-agents, caching, and compaction
• Build evaluation pipelines and demonstrate system 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 transfer learnings across engagements
• Engage early in projects to shape use cases and assess feasibility
• Write production-grade Python including 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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