Model teams can ship a demo in a week. The harder test starts when ten agents, three product teams, and sensitive company data must share the same runtime without turning every release into a custom rescue job.

That shared layer belongs to AI platform engineering. The work includes architecture judgment around model access, orchestration, deployment paths, identity, observability, cost control, rollback, and the boundary between a reusable platform capability and a product-specific decision.

At TeamStation, Axiom Cortex tests that judgment before an AI Platform Engineer enters client work. The evidence path combines production history with reasoning, communication, ownership, architecture decisions, AI workflow fit, and delivery readiness. A tool list can show exposure. It cannot show whether the engineer knows what should become a platform rail, what should stay local, or how to explain the tradeoff.

The AI Platform Engineer route lays out the public evaluation categories and the operating controls around the engineer. In a distributed LATAM team, those decisions compound fast because every product team consumes the platform underneath its work.

https://teamstation.dev/hire/by-role/ai-platform-engineer