OptiResume

How to become a AI / ML Engineer

Build systems that learn: the maths, the modelling, and the engineering that gets a model into production.

24-34 weeks4 phases17 steps31 free resources3 checkpoint tests

Best for

Students who genuinely enjoy maths and experimentation and are willing to accept a longer runway than other paths need.

Maybe not for you if

If you dislike statistics, or want the fastest route to a first offer, backend or data engineering will get you hired sooner. ML is typically the most competitive of these paths.

The 4 phases

What each phase gets you to. The steps, resources and checkpoint inside each one open with a free account.

  1. Phase 1 - Python and the maths that matters

    5-7 weeks

    Goal: Write confident Python and reason about vectors, distributions and gradients.

    4 steps · ends in a scored checkpoint (60% to clear) · assumes basic programming and school-level maths

  2. Phase 2 - Classical machine learning

    6-8 weeks

    Goal: Train, evaluate and explain a model on real data without a tutorial open.

    5 steps · ends in a scored checkpoint (60% to clear)

  3. Phase 3 - Deep learning and deployment

    7-10 weeks

    Goal: Train a neural network, build an LLM-backed service, and serve a model behind an API.

    4 steps · ends in a build deliverable

  4. Phase 4 - Engineering fundamentals and the process

    6-9 weeks

    Goal: Clear the coding and CS rounds ML candidates are still asked to pass.

    4 steps · ends in a scored checkpoint (60% to clear)

Open the full AI / ML Engineer roadmap

Free account, no card. It takes about a minute and you do not need to verify your email to start.

  • All 17 steps, in order, with why each one is there
  • 31 hand-picked free resources, no paid course upsells
  • The specific mistake people make at each stage
  • 3 scored checkpoint tests, so progress is earned not ticked
  • Progress saved per step, so a break does not cost you the thread
  • Adaptive start, phases your test history already clears are skipped
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What hiring actually looks like here

AI and ML literacy is now commonly listed across a large share of fresher postings, but dedicated ML roles typically remain more competitive than backend or data engineering. Many people enter ML from a software or data role rather than directly. A growing share of openings labelled AI engineer are really LLM integration plus solid backend work; Phase 3 prepares you for exactly that.

We claim no placement outcome, salary or success rate. This describes what is commonly reported about the role, nothing more. Linked resources are credited to their authors.

Comparing paths?