An AI agent crosses a line at runtime, not in the policy meeting. The governance engineer has to know what called the tool, which data it touched, who approved the action, and where execution stops.

We test risk reasoning, policy-control judgment, audit-readiness thinking, ownership, communication, and architecture judgment. The interview keeps video, transcript, question mapping, per-answer evidence, B-Axiom scoring, human calibration, risk notes, and final recommendation. Governance skill becomes a client-visible evidence locker instead of another title.

The AI Governance Engineer role route connects candidate judgment to operating controls. Nebula maps role and production signals. Axiom Cortex tests how the engineer reasons under pressure. The operating layer carries identity controls, MDM, secure onboarding, escalation paths, delivery risk, and telemetry.

Across LATAM delivery, control ownership cannot dissolve across borders or time zones. A policy has to survive the real loop: agents, data, devices, humans, approvals, and production evidence.

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