MLOps / AI platform engineer
Keep AI services deployable, observable and recoverable.
The outcome this role owns
Teams can release and operate models with controlled access, repeatable deployments and a tested response to failure.
Four levels of maturity
Proposed framework · Evidence-based discussion · Not a validated assessment scale
Foundation
Deploys a test service and records its model, configuration and dependencies.
Evidence to look for
A reproducible test deployment
Pilot
Creates repeatable environments with basic monitoring, access controls and deployment checks.
Evidence to look for
A deployment pipeline that blocks a known bad release
Production
Operates live services with defined reliability targets, rollback procedures and cost alerts.
Evidence to look for
A rollback drill and service dashboard
Organisational
Runs a shared platform with measured developer adoption, capacity planning and tested recovery across teams.
Evidence to look for
Platform adoption and reliability results across teams
A practical work sample
Give a scenario where a model update increases latency and changes output quality. Ask for detection, containment, rollback and the evidence needed to try again.
Use synthetic or public data, explain the evaluation criteria in advance and allow reasonable adjustments. Discuss the reasoning as well as the finished artifact.
What to question
The service can deploy, but there is no known working version to restore or owner for an incident.
Related market signal: ict specialists
ICT employment is a proxy for adjacent infrastructure skills. It does not identify people experienced in operating AI systems.
Highest observed shares among covered markets, 2025. These are context, not recommended hiring locations.
Eurostat source and definitions ↗
Salary context
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Explore related salary evidence ↗Framework sources
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