Become an AI Engineer in 6-9 months

AI ENGINEER IN 6-9 MONTHS 01 STEP Your Missing CS Degree • Python is the backbone. Every ML framework, every data pipeline, every automation script. • Git, SQL, Docker, Linux, testing, CI/CD. Not ML topics, but every ML job requires them. • Spend 2-3 months on this groundwork before you touch any models. RESOURCES "Python Crash Course" by Eric Matthes "Clean Code in Python" by Mariano Anaya "The Missing Semester" MIT (missing.csail.mit.edu) "Pro Git" by Scott Chacon (free) 01 BUILD Foundations: Skills to Build Build a Python ETL script: API to database Containerize an app with Docker and deploy it Set up CI/CD with GitHub Actions Write SQL queries: joins, aggregations, window functions Get comfortable with Linux, shell scripting, logging, and testing Put everything on GitHub. Even the ugly stuff. AI ENGINEER IN 6-9 MONTHS 02 STEP Learn Enough Math • Linear algebra, probability, basic calculus. That's the core list. • You don't need a degree. You need to know what a gradient does and why it matters. • If you can't explain your model's loss function, you don't understand your model. RESOURCES "Mathematics for Machine Learning" by Deisenroth, Faisal & Bruce "Practical Statistics for Data Scientists" by Bruce & Bruce 3Blue1Brown Essence of Linear Algebra 02 BUILD Math: Skills to Build Code linear regression from scratch, no sklearn Derive backprop on paper, then implement it Know when to use Bayesian vs. frequentist approaches Design and run a proper A/B test with significance Plot everything. Understanding starts with visualization. If you can code it from scratch, you understand it. AI ENGINEER IN 6-9 MONTHS 03 STEP Learn the Algorithms • Trees, SVMs, gradient boosting, k- means, PCA. Know these cold. • Then go deeper: CNNs, RNNs, attention, transformers. • Don't just call model.fit(). Understand what happens inside. RESOURCES ML & DL Specializations by Andrew Ng (Coursera) "ML with PyTorch & Scikit-Learn" by Raschka "Hands-On ML" by Géron 03 BUILD ML/DL: Skills to Build Code a neural net from scratch: forward pass, backprop, updates Train an image classifier on your own data, not MNIST Build a full pipeline: raw data -> clean -> train -> evaluate -> serve Enter a Kaggle competition and actually submit Pick one paper. Reproduce the results. Kaggle teaches what courses skip: messy data and real scoring. AI ENGINEER IN 6-9 MONTHS 04 STEP Ship to Production • Most ML models die in a Jupyter notebook. MLOps is why they shouldn't. • If you can train it but can't deploy it, it's a school project. • Companies hire people who ship, not people who only train. RESOURCES "Designing ML Systems" by Chip Huyen Made With ML madewithml.com deep-learning-blogs.vercel.app 04 BUILD MLOps: Skills to Build Deploy a model behind an API on AWS, Azure, or GCP Track experiments with MLflow or Weights & Biases Version your data and models with DVC Build a CI/CD pipeline that retrains on new data Set up drift detection so you know when your model goes stale Pick one cloud platform and go deep. The skills transfer. AI ENGINEER IN 6-9 MONTHS 05 STEP Learn LLMs & GenAI • This is where the hiring is. Every company is building with LLMs right now. • Calling an API isn't engineering. Understand tokenization, attention, fine-tuning. • RAG, agents, MCP, A2A, evaluation. These are the real engineering problems. RESOURCES "LLM Engineer's Handbook" by Paul lusztin "AI Engineering" by Chip Huyen "Build a Large Language Model from Scratch" by Raschka 05 BUILD LLMs: Skills to Build Fine-tune Llama or Mistral on a custom dataset Build a RAG pipeline with chunking, embeddings, and retrieval Ship an LLM app that real people use, not a Gradio demo Set up LLM evaluation and observability (Langfuse, Opik) Build an MCP-powered agent with tool use and multi- step reasoning A deployed app beats ten notebooks in any interview. Follow For More AI & Coding Content