What You'll Actually Do

• Design and build the architecture for AI agent workflows — planning loops, tool use, memory, retrieval, and human-in-the-loop checkpoints
• Evaluate, integrate, and fine-tune foundation models and LLM APIs for specific enterprise use cases and data types
• Define standards for agent reliability, observability, and failure modes in production deployments
• Collaborate with Forward-Deployed Engineers to translate what's working in client environments into reusable platform components
• Build internal tooling and eval harnesses to assess agent quality, hallucination rates, and task completion
• Make principled, documented architectural decisions — and stay current enough with the ecosystem to know what to adopt and what to ignore

Who We're Looking For

• 6–10 years building production AI or data systems — not prototypes; systems that run reliably at scale under real conditions
• Deep hands-on experience with multi-agent architectures: context windows, memory management, dependency graphs, and where things break in practice
• Strong Python and familiarity with agent frameworks — LangChain, LlamaIndex, AutoGen, or equivalent — or a clear, documented opinion on why you built your own
• Practical experience with RAG architectures, vector databases, and context window management in production settings
• Experience deploying LLM-powered systems in enterprise contexts — data security, access controls, audit logging

The Stuff That's Harder to Teach

• LLM failure mode literacy. You know the accuracy cliff. You know why prompting cannot fix semantic problems. You build systems that don't rely on the model getting it right every time.
• Production instincts. You don't consider something done until it's been wrong three times and you've fixed it twice — and you've built the judgment to know when to ship anyway.
• Strong opinions on agent design. You have a clear answer to why most agent architectures fail at enterprise scale — and you've built something that doesn't.
• Systems thinking. You design for failure modes first. Happy paths are not the interesting problem.

Compensation

• Competitive base salary and meaningful early-stage equity. This is a foundational technical role and we price it that way. We'll be transparent about the full picture in our first conversation.