Lead AI Engineer with .NET

Responsibilities

• Own the architecture of AI features and ship them, including natural-language query, summarization, intelligent validation and exception handling, and workflow assistance
• Establish and evangelize reusable AI feature patterns for retrieval, evaluation and guardrails so teams do not reinvent them 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

Languages