Architecting hybrid org structures that blend ML engineers, applied scientists, prompt designers, evaluators, and domain experts. In 2026, with global AI talent
Building and scaling AI teams is organizational design under talent scarcity: defining the role mix (engineers, evaluators, product, governance), hiring against demonstrated craft rather than credentials, structuring hub-and-spoke delivery, and growing internal talent when the market won't supply it.
Leadership-critical: AI talent competition stays brutal, and leaders who can assemble and keep delivery-capable teams are the binding constraint on most enterprise AI ambitions.
Small and senior: a product-minded AI engineer pair, an evaluation-strong builder, data support, and a delivery lead, proving value patterns before headcount scales. Big-bang AI orgs precede big-bang disappointments.
Test the craft: take-home or pairing on retrieval/eval problems, portfolio walk-throughs of shipped systems. Demonstrated production judgment routinely beats credential-rich resumes, and prices more honestly.