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MLOps Engineer

Keep models healthy, fast, and cheap in prod.

You're the SRE for ML. You build the infra that lets data scientists deploy without breaking things, and you make sure inference stays reliable, observable, and inside budget.

Start the recommended pathCompare other roles

Skills profile

What hiring managers actually weigh.

Recommended paths

Take them in order. Each builds on the previous.

1

AI Engineering Foundations

The shortest path from software engineer to AI engineer.

2

Deployment & MLOps

Get your model off the laptop and into production.

3

RAG & Vector Databases

Make models answer from your data, not their guesses.

90-day roadmap

An honest, week-by-week plan. Adjust to your weekly time budget.

  1. 1

    Cloud + DevOps base

    ~4 weeks
    • Containers, K8s, IaC
    • CI/CD for ML repos
  2. 2

    Serving & scale

    ~4 weeks
    • vLLM / TGI / Triton
    • Autoscaling + GPU scheduling
  3. 3

    Observability

    ~3 weeks
    • Drift detection
    • Cost dashboards
    • Pass MLOps cert

Certifications worth taking

Practice & certify on CertQuests after completing the course.

Career outcomes

Realistic next steps after completing the roadmap.

  • Pivot from SRE / DevOps
  • High-leverage senior roles
  • Lead Platform/ML at scale-ups