AI · S/06
The AI infrastructure surge is real. The hiring committees that win are the ones who hunt for leaders who have shipped, not just published.
Why this sector
AI leadership hiring is a candidate-universe problem more than an industry problem. The companies hiring Heads of AI, Chief AI Officers, and VPs of AI Engineering span every sector — what's distinct is the talent pool. The right hire has shipped models to production under real latency and cost constraints, knows when to build versus buy, and can recruit a team in a market where the best operators field three offers a week. We hunt that talent universe — research-to-production engineers, applied ML leaders, and AI product executives who can ladder strategy into shipped systems.
What we look for
The wrong AI hire burns 12-18 months and a recruiting budget. The right one ships the first system inside a quarter and starts compounding the team.
Operators who can ship a model from research to production under real latency and cost constraints.
Pragmatic build-versus-buy judgment — knows when foundation-model APIs win and when to own the stack.
Track record recruiting and retaining ML talent in a market where the best candidates field competing offers.
Experience setting up MLOps, evaluation, and observability — not just training notebooks.
Ability to translate AI capability into measurable business outcomes the board can underwrite.
Comfort partnering with security, legal, and policy on data, IP, and model-governance questions.
Roles we commonly fill
Placeholder · awaiting client copy
Anonymous Case Study
{/* TODO: Gonzalo case study (D14 follow-up) — awaiting client copy for AI placement */} A growth-stage company had a research-strong ML team that couldn't move work into production. Quarterly board updates kept landing the same demos.
We hunted a Head of ML Platform with a track record of standing up MLOps and evaluation infrastructure inside two prior companies — someone who could rebuild the production path without breaking the research culture.
Within two quarters, three models that had lived in notebooks for a year were serving traffic in production. The team began compounding on infrastructure rather than rebuilding from scratch per project.
Tell us the AI seat you're trying to fill. We'll hunt the leader who actually ships.
