Staff AI Engineer

This is a heavy-hitting Staff AI Engineer role working with Microsoft's internal AI engineering team, split squarely between writing code and steering technical direction. If you're tired of just building proof-of-concept wrappers around LLM APIs and actually want to ship production-grade, highly scalable AI architectures, this is worth looking at.

What You'll Actually Be Doing

You'll be jumping into ambiguous problem spaces, translating messy enterprise requirements into concrete technical designs, and then rolling up your sleeves to write code alongside the team. Your day-to-day will involve designing robust RAG pipelines, building agentic workflows, figuring out how to optimize token costs and latency in production, and mentoring other engineers to level up their AI systems design.

The Core Tech Stack

You need deep fluency in Python and modern AI paradigms like LLMs, vector embeddings, and multi-agent systems, balanced with solid full-stack fundamentals including PostgreSQL, SQL, and React or C#. The team isn't just looking for someone who knows how to prompt-engineer; they need someone who understands distributed systems, production observability, and how to evaluate AI output quality systematically.

Interview Expectations

Expect the hiring managers to dig deep into your failure stories, asking you to walk through a time an LLM-based system failed in production and how you handled latency, hallucinations, or runaway costs. They want to see that you understand the nuances of AI system trade-offs rather than just treating models like black boxes. Be ready to defend your architectural decisions regarding model selection, chunking strategies for RAG, and state management in agentic loops.

Application Advice

Your resume needs to scream production maturity. Make sure you explicitly highlight systems you've built from scratch (0-to-1) and deployed to actual users, dropping keywords like 'RAG', 'agentic workflows', 'AI evals', and 'production observability'. Leave off the toy projects and API wrappers—focus heavily on how you scaled architecture, handled cost constraints, and influenced technical direction.