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ML Engineer

Take models from notebook to production.

You bridge data science and software engineering. You build training pipelines, manage data, and own models in production. You're the one paged when accuracy drops at 3am.

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

LLMs & Transformers

See inside the model — without the math wall.

3

Fine-tuning & Adaptation

When prompting isn't enough.

4

Deployment & MLOps

Get your model off the laptop and into production.

90-day roadmap

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

  1. 1

    Foundations

    ~4 weeks
    • Python+NumPy fluency
    • Train classical models
    • Vector intuition
  2. 2

    Modern ML

    ~5 weeks
    • Transformers internals
    • Fine-tune one model end-to-end
    • Build a clean training pipeline
  3. 3

    Production

    ~4 weeks
    • Deploy + monitor
    • Cost & scale
    • Pass a cloud ML cert

Certifications worth taking

Practice & certify on CertQuests after completing the course.

Career outcomes

Realistic next steps after completing the roadmap.

  • Mid → senior MLE roles
  • Pivot from SWE / Data
  • Path toward Staff ML / Tech Lead