Employer description & source details
You've built software systems hands-on and shipped 0→1 products or platforms into production, often without a spec. You've shipped agents or LLM systems that real people use. When one went wrong, you traced it back to the context, tool call or decision that caused it. You use evals to find and fix real problems, check what a score actually counts, and judge agents by user impact: hours saved, error rate or cycle time. You design for failure first: what the agent may touch, what happens when it's wrong, and where a person approves before it acts. You build with coding agents every day and hold what they write to the same bar as your own code. You're a critical optimist. You believe what agents can do is real, and test what still fails before trusting them with more. You reason from first principles. When a familiar design doesn't fit, or a better model makes old scaffolding unnecessary, you notice and rebuild. You have strong systems judgment. You can design for production reliability, latency, cost and security, build it yourself, and leave it in a shape others can build on. You do your best work owning an initiative with one or two people alongside you, and don't need a large team to feel effective. Experience with MCP, A2A, agent frameworks and runtimes, LLM gateways or agent observability, or you've built an agent harness or orchestration layer yourself. Experience with knowledge graphs, vector search, retrieval or semantic layers. You've built an eval suite from scratch, or run shadow evaluations, LLM grading or staged rollouts. You've shipped agents that write to a system of record, with permissions, approvals and an audit trail. You've led AI adoption inside an engineering or business team and can show the before and after. Founding-engineer, technical-founder or
Extracted skills: recruiting, security