Were an engineer, moved to product to own outcomes, and never stopped building
Think like an architect and can design and stand up a self-optimizing system across discovery, onboarding, and integration
Have felt, first-hand, how an AI agent succeeds or fails at a real integration, and have strong opinions about why
Want to own a product that is becoming the front door of the business, at the moment it is becoming that
Are energized by being early — defining the practice, not inheriting it
Excellent product management judgment. You own product and roadmap, set direction, decide under uncertainty, ship outcomes, and lead cross-functional work without authority. You can show the results
A former engineer's depth (required). You were a senior software engineer, or more, before you moved to product. You architect and ship production systems, you read and write real code, and you reason with engineering at their level. You are not a vibe coder who assembles what a tool generates
Deep AI fluency, proven by shipped work (required). You have personally built and shipped AI software that goes well beyond prompt files and markdown — agents, MCP servers, CLI tools, agent and evaluation harnesses, real model-integrated tools — and it is public. Send us the GitHub; we will read the code, the commits, and the design
Proven ability to stand up a complete system from scratch — the rhythms of business, the reporting and optimization, the experimentation platform — yourself or in-house, or by researching and deploying the right tools
PLG and developer-product fluency. You understand how developers, and increasingly their agents, adopt APIs, and you understand product-led growth
The judgment to distrust a number or a passing test before you build on it. You ask whether it is real, as a reflex
Clear communication with executives: you lead with the decision, keep your method in reserve, and hold up under pushback without either caving or digging in
Built specifically for AI agents as the consumer — MCP servers, agent harnesses, CLI tools, agent-readable docs, tool definitions, or evals for agent output
Experience with voice, audio, or real-time streaming systems
A track record of open-source work with real adoption
Time in a company with both a self-serve and an enterprise motion
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