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We are seeking a Lead AI Engineer to drive the integration of AI-based features into measurement products. In this role, you will own the architecture of these features and ship them against an established AI/data platform, rather than building the platform itself. Your work will span natural-language query, summarization, intelligent validation and exception handling and workflow assistance, integrating with a model gateway, prompt registry, retrieval services and vector stores. Since outputs feed regulated measurement workflows, evaluation, auditability and human-in-the-loop patterns are central to the job, and you will be accountable for the work of a small engineering team.
- 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
- Instrument telemetry via Application Insights and Serilog
- 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