Hiring nowMLOps and AI platform engineering

Model Registry and Release Engineer

Also called Model Release Engineer

A Model Registry and Release Engineer keeps every model artefact traceable and every deployment approved. It matters because a company that cannot show what is running, and who signed it off, cannot release safely or answer an audit.

What they do

They own the registry and the path from trained model to production. They define how artefacts are versioned, tagged and promoted between stages. They wire approval checks into CI/CD so nothing deploys without the right sign-off. They record lineage: which data, code, configuration and base model produced each artefact. They build the release evidence that shows what changed and who approved it. They work with ML engineers on packaging in containers, with platform teams on orchestration, and they join incident reviews when a release goes wrong. They also manage rollbacks and retire old versions.

What a strong hire delivers

In the first months, a strong hire gives the company a single registry that teams actually use. Every production model can be traced back to its source. Promotion between stages follows a defined, enforced approval path. Release evidence is produced automatically rather than assembled by hand. Rollback is tested and quick. Unauthorised or untracked releases are blocked, and teams stop relying on shared drives and memory.

How you can tell they are good

Ask them to walk through a release path they built, from training run to production. Strong people can name exactly where lineage is captured and where approval is enforced, not just recorded. Ask what happens when someone tries to bypass it. Ask for a rollback they had to run. Worry if they describe a registry as a storage folder or lean only on process documents.

Why it is hard to fill

The title is rare and the work usually hides inside other jobs. Most candidates are platform, DevOps or ML engineers who did this as part of a wider remit. Few have owned lineage and approval integration end to end. Job titles vary widely, so searching by title misses people. We read for the work done, not the label on the CV.

Where to find them

Look at platform engineers, DevOps and release engineers who have supported ML teams, and ML engineers who ended up owning deployment. They come from AI product companies, larger engineering organisations with regulated or audited releases, and teams running many models in production. Experience with CI/CD, containers, orchestration and a model registry matters more than the title they held.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Model Registry and Release Engineer

  1. Describe how you would trace a production model back to its training data, code and configuration.

    A strong answer covers capturing lineage automatically at training and packaging time, immutable artefact identifiers, and linking data and config versions. Worry if lineage depends on people filling in notes or naming conventions.

  2. How have you enforced approval before a model reaches production, rather than just recording it?

    Look for gates in CI/CD, role-based promotion rights, and checks that fail the pipeline. Good answers mention audit trails. Worry if approval is a chat message or a manual ticket nobody verifies.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring a Model Registry and Release Engineer to make sure every model we ship is traceable, approved and reversible. You will own the registry and the release path, and work closely with ML and platform engineers so releases are safe without being slow.

What you will do

  • Own the model registry and its versioning, tagging and promotion rules
  • Integrate approval checks into CI/CD so unapproved models cannot deploy
  • Capture artefact lineage across data, code, configuration and base models
  • Produce release evidence records for each deployment
  • Maintain and test rollback procedures
  • Package models in containers and support orchestration on our platform
  • Take part in incident reviews and improve the release path afterwards

What you will bring

  • Built or run a model registry or comparable artefact system in production
  • Implemented automated deployment gates in CI/CD
  • Solid experience with containers and orchestration
  • Worked on model lifecycle problems with ML engineers
  • Handled production incidents and rollbacks
  • Can explain design choices clearly to engineers and non-engineers

Nice to have

  • Experience with MLflow or a similar registry
  • Background in audited or regulated release processes
  • Experience supporting several ML teams on a shared platform

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • MLOps and model lifecycleAdvancedVersion, release, monitor and retire models
  • Cloud and orchestrationAdvancedProvision and operate scalable compute services
  • Distributed systemsWorkingDesign reliable work across networked machines
  • Reliability and incident responseWorkingDetect failures, restore service and prevent recurrence

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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