An AI governance engineer maintains model cards and approval workflows in the city’s model registry. What does this role support?

AI governance engineering that executes policy via registry artifacts and approvals is what maintaining model cards and approval workflows in the city’s model registry supports. Governance engineers turn written policy into operating machinery: required metadata, evidence attachments, staged approvals, and traceable promotion states. For municipal AI, that means a department cannot treat production as an informal side door—the registry records what was approved, under which conditions, and with which documented risks.

AI governance engineering that improves accuracy via registry artifacts and approvals is not supported by maintaining cards and approvals; those artifacts and approvals enforce governance policy, they do not optimize model performance. AI governance engineering that provisions datasets via registry artifacts and approvals is a different function; the registry is an approval and evidence system, not a storage or provisioning service for training data. AI governance engineering that monitors drift via registry artifacts and approvals is a separate operational concern; model cards and staged approvals govern promotion and compliance, not runtime observability.

Exam caveat: a registry that is incomplete or optional does not equal governance; exams look for operating enforcement, not just the existence of a tool. Operational check: make production deploy keys contingent on registry-approved states, require model cards for each municipal model version, reconcile running endpoints to registry entries monthly, and escalate shadow AI found outside the approval workflow.