Chief Forward Deployed Engineer

EPAM is a global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and startups. They specialize in custom software, product, and platform engineering to empower clients to become AI-Native enterprises.

• 7+ years engineering experience with a strong recent track record in production AI/LLM applications
• Expertise in agent design focusing on task-harness fit and failure handling
• Ability to lead discovery and feasibility conversations directly with clients and SMEs
• Hands-on experience with agentic frameworks such as LangChain, LangGraph, or Semantic Kernel and major LLM providers (OpenAI, Anthropic, Google Gemini)
• Expert-level Python skills and solid software engineering fundamentals
• Strong RAG and retrieval skills including vector databases, embeddings, hybrid search, re-ranking, chunking, and context management
• Experience with generative AI evaluation methods and observability tools like LangSmith, Arize Phoenix, or Langfuse
• Production deployment experience on major clouds (AWS, Azure, GCP) with containerization and CI/CD
• Good judgment under ambiguity to balance speed, quality, and scope
• English proficiency at C1 level

Nice to have:

• Experience designing experiments, A/B testing, and iterating AI products
• Background in NLP, Data Science, or applied ML with production deployment experience
• Familiarity with MCP, A2A, Agent Skills, and emerging agent standards
• Experience with enterprise AI platforms such as AWS Bedrock AgentCore, Databricks Genie, or Microsoft Foundry
• Exposure to AI governance, security, and compliance, including guardrails and prompt-injection prevention
• Prior client-facing or pre-sales consulting experience

• Design, build, and deploy AI-native end-to-end systems including agents, workflows, RAG, custom tool calling, sandboxing, context engineering, caching, and compaction
• Develop evaluation pipelines to verify system effectiveness
• Handle agent loop failure modes such as retries, model fallbacks, cost limits, and human-in-the-loop interventions
• Capture and replicate domain expertise and workflows across engagements
• Engage early with stakeholders to shape use cases and assess technical feasibility
• Write production-grade Python code for integrations, APIs, data access, and deployments
• Collaborate directly with SMEs and end-users through interviews, UAT, and workflow observations to validate system fit

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