ai engineer with .NET for measurement products

The team integrates AI-based features into measurement products, using an established AI/data platform to support regulated measurement workflows.

• Own the architecture and delivery of AI features, including natural-language query, summarization, intelligent validation and exception handling, and workflow assistance
• Establish reusable AI feature patterns for retrieval, evaluation and guardrails
• Implement RAG end to end, including 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 practices, including golden datasets, offline evaluation 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, and take accountability for its 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 practices, 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 experience 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 modern AI/data platform components and how to build against them, including model gateways, prompt registries and vector stores
• Familiarity with embedding pipelines, evaluation frameworks, LLM observability and guardrails
• Sound judgment about where AI adds product value and where deterministic logic is preferable
• Solid SQL fundamentals and API integration skills
• Strong documentation and standards habits, including architecture documentation 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
• Ability to work in agentic automation pipelines, including AI-driven PR review and QA acceptance flows triggered by ADO work item tags
• English proficiency at Upper-Intermediate level (B2) or higher

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