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Machine Learning Engineer

Machine learning engineers take models from experiments to production: they build data pipelines, train and evaluate models, and deploy and monitor them in real applications, including systems built on large language models. The role combines strong software engineering with ML knowledge.

What the job involves

  • Build pipelines that prepare training data.
  • Train, evaluate and tune models.
  • Deploy models behind APIs and monitor their quality over time.
  • Integrate AI features, including language models, into products.

Core skills

  • Python and software engineering
  • Data structures and algorithms
  • SQL and data pipelines
  • Machine learning and deep learning
  • APIs and deployment
  • Math: linear algebra, probability, statistics

Your learning path

Work through the steps in order. Each one links to the lessons and practice to use.

  1. Step 1: Python, properly

    ML engineers write production Python, not just notebooks. Learn OOP, modules, generators and error handling.

  2. Step 2: Software engineering fundamentals

    Data structures, algorithms and clean code matter as much here as in any engineering role.

    Not on Laviyx Learn yet

    • Git and code review
    • Unit testing with pytest

  3. Step 3: Data and SQL

    Models are only as good as their data. Query, join and validate data reliably.

  4. Step 4: Machine learning and deep learning

    Learn the core algorithms, how to evaluate them honestly, and neural networks.

    Not on Laviyx Learn yet

    • Linear algebra, probability and statistics

  5. Step 5: Serving and operating models

    A model creates value once it runs reliably in production.

    Not on Laviyx Learn yet

    • Docker
    • A cloud ML platform

  6. Step 6: Prepare for interviews

    Expect coding rounds, ML concept questions, and an ML system design discussion.

Projects to build

Projects prove your skills and give you something to show.

  • Train a text classifier and serve it behind a small API.
  • Build a data pipeline that retrains a model on fresh data and reports its accuracy.
  • Build a question-answering tool over your own documents using a language model API.

Interview preparation

This path is a suggested order. Requirements differ between companies, so also read job descriptions for the roles you are applying to.

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