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, the company empowers 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
• Strong agent design judgment focusing on task-harness fit considering context, failures, and policies
• Ability to lead discovery and feasibility discussions and communicate technical trade-offs effectively to both technical and non-technical audiences
• Hands-on experience with agentic frameworks such as LangChain, LangGraph, or Semantic Kernel
• Experience with major LLM providers including OpenAI, Anthropic, and Google Gemini
• Expert-level Python skills and solid software engineering fundamentals
• Strong retrieval and RAG skills, including vector databases, embeddings, hybrid search, re-ranking, chunking, and context management
• Proven experience in generative AI evaluation, including LLM-based evaluation and use of observability/tracing tools like LangSmith or Arize Phoenix
• Production deployment experience on major clouds (AWS, Azure, or GCP) with containerization and CI/CD
• Sound judgment under ambiguity in scoping, sequencing, and 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 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, Retrieval-Augmented Generation (RAG), custom tool calling, sandboxing, context engineering, sub-agents, caching, and compaction
• Build evaluation pipelines to validate system usefulness
• Design for failure handling in the agent loop, including retries, model fallbacks, cost limits, and human-in-the-loop for consequential actions
• Capture domain expertise and repeatable workflows to transfer knowledge between engagements
• Engage early in projects to shape use cases and assess technical feasibility
• Write production-grade Python code for integrations, APIs, data access, and deployment
• Work directly with SMEs and end-users through interviews, user acceptance testing (UAT), and observation to validate system alignment with real workflows

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