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MLOps Engineer

You make machine learning repeatable — training, tracking, deploying and watching models in production.

Degree usually expected Moderate for someone with production infrastructure experience.


Can I actually do this?

Not a first job. It is largely DevOps discipline applied to models, so the transfer from platform or infrastructure work is direct — that is our reasoning from the skill overlap, not a hiring source we have checked.

Who it suits. People who already care about reproducibility and monitoring, and want to apply that where the artifact is a model rather than a binary.

Runway. Realistic from a DevOps, platform or backend background. The MLOps-specific layer is months.

Coming from another job?

Coming from DevOps, platform or cloud engineering? Most of what you know applies; the new part is that the artifact drifts after you ship it. Cloud Engineer Data Engineer (AI/ML)

Also advertised as

  • ML Platform Engineer
  • ML Infrastructure Engineer
  • Machine Learning Ops Engineer

The route

Four stations, in order. Each one is a thing you finish before the next matters.

  1. Station one

    Learn it free

    Only the best few, deliberately. Every one of these is free to use — the pill on each card says exactly what is and isn't free.

    DataTalksClub MLOps Zoomcamp

    Free to learn · no certificate

    Full open MLOps course, free in both modes. IMPORTANT NUANCE from their own comparison table: the certificate belongs to the LIVE COHORT, which is "Not currently scheduled" — they state "We don't plan to run a live cohort in 2026". Self-paced study is available anytime and issues NO certificate. Their stated prerequisite is 1+ year of programming experience.

    Made With ML — MLOps course

    Free to learn · no certificate

    Design, development and production of ML systems. Published by Anyscale (the page brands itself "Made With ML by Anyscale"); the course material is free, though the site also promotes Ray credits.

    MLflow documentation

    Free to learn · no certificate

    Experiment tracking, model registry and deployment — the open-source tooling most MLOps job ads assume familiarity with. Free and open source; no credential. Use this directory URL: the /index.html variant returns a near-empty page.

    See the full catalog in the explorer →

  2. Station two

    Attest strategically

    We found no credential worth paying for that is specific to this role. That is a finding, not an omission: the tooling here is open source, and a repository showing a tracked, reproducible pipeline is the evidence employers can actually check.

    Nothing here is worth paying for

    No credential needed

    We found no credential worth paying for that is specific to this role. That is a finding, not an omission: the tooling here is open source, and a repository showing a tracked, reproducible pipeline is the evidence employers can actually check.

  3. Station three

    Prove it

    A certificate says you passed a test. These say you can do the job.

    A reproducible training pipeline

    Track experiments, version the data and register the model, so someone else can reproduce a run from your repository alone.

    A deployment with a rollback you have used

    Ship a model behind an API with versioning, then actually roll one back and document what happened.

    Drift detection that fired

    Monitor a deployed model, feed it shifted data, and show the alert firing. A monitoring setup nobody has ever seen trigger is untested.

  4. Station four

    Get hired

    Search these exact titles

    • MLOps engineer
    • ML platform engineer
    • ML infrastructure engineer

    Who hires for this. Companies running models in production — product firms, banks, retailers, and AI platform teams.

    The repository is the interview. Reproducibility, versioning and rollback are inspectable in a way that a course completion is not.

    On salary

    We don't publish salary estimates. Numbers copied between blogs drift from reality, and a wrong number costs you real negotiating power. When we have a verified public source, it goes here with its date.


Where this route continues

Continues into AI platform and infrastructure engineering.

· How we verify