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# TELECOMMUTE Lead AI Engineer with .NET

- **Company:** EPAM Systems, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, C Sharp (Programming Language), Code Review, Data Infrastructure, Fault Tolerance, Python (Programming Language), Open Web Application Security, Systems Development Life Cycle, OpenAI, Software Engineering, SQL Databases, Data Streaming, GitHub Copilot, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, AI Coding Agents, Wikis, Claude
- **Published:** October 11, 2026
- **Apply:** https://www.dice.com/job-detail/78095764-a441-45df-9c5f-8d2f142ca105

## About the Role

Insights and Serilog Requirements 8+ years of software engineering experience in .NET (C#) and/or Python, with lead-level ownership of AI feature architecture across one or more products Proven background in shipping LLM-based features to production, including tool and function calling, structured outputs and streaming Proficiency with Azure OpenAI / Azure AI Foundry, OpenAI or Anthropic APIs Working knowledge of the component parts of a modern AI/data platform and how to build against them: model gateway, prompt registry, vector stores Familiarity with embedding pipelines, evaluation frameworks, LLM observability and guardrails Judgment about where AI adds product value and where deterministic logic is the better tool Solid SQL fundamentals and API integration skills Strong documentation and standards habits, including architecture docs in Azure DevOps Wiki, code review and testing discipline Hands-on experience using AI coding agents in the SDLC such as GitHub Copilot, Claude or
equivalent Capability to work in agentic automation pipelines, including AI-driven PR review and QA acceptance flows triggered by ADO work item tags English proficiency at an Upper-Intermediate level (B2) or higher

## Description

per product Implement RAG end to end: chunking, embeddings, hybrid search, reranking, and grounded responses with citations Drive prompt and context engineering, including multi-step and agentic flows where they add product value Define evaluation discipline, including golden datasets, offline eval suites, LLM-as-judge approaches and per-release regression checks Define and document complex requirements with stakeholders across product features, evaluation criteria and responsible-AI constraints Lead and mentor a small engineering team on LLM integration and evaluation, taking accountability for their work Manage cost, latency and reliability through caching, fallbacks, token budgets and graceful degradation Integrate with platform services such as model gateway, prompt registry, vector stores and embedding pipelines Apply responsible-AI practice, including tenant data isolation, auditability, OWASP LLM Top 10 mitigations and human-in-the-loop patterns Instrument telemetry via Application