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Growth Paths / Become an AI Engineer
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Become an AI Engineer: A Roadmap Built on Shipped Work

From zero to shipping AI products that real people use.

A structured 24-week path from programming fundamentals to shipping a production AI product with real users. Every milestone is verified by proof, not self-assessment. This path mirrors what working AI engineers actually do — not what courses teach.

5 required outcomes24 weeksCredential on completion

Your portfolio at the end

Path outcomes

Skills

First Three Projects Shipped (Beginner to Public App)

Every AI engineer starts with a working Python environment and basic programming intuition. Skip this if you already write Python daily.

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5 milestones

  1. 1Write and run your first Python program
  2. 2Build programs using data structures
  3. 3Write functions and handle errors
  4. 4Build a real project from scratch
  5. 5Ship something others can use
Skills

Master Python for Real Projects

Python is the lingua franca of AI engineering. You need to be fluent before you can reason about what a library is doing under the hood.

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5 milestones

  1. 1Write Pythonic code, not just Python
  2. 2Work with APIs and external libraries
  3. 3Write tests for your own code
  4. 4Build a data pipeline from files
  5. 5Publish a Python package on PyPI
Skills

Build and Deploy Your First Web App

AI features live inside products. Building a web app first teaches you how to wire an LLM response into a UI that real users interact with.

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5 milestones

  1. 1Understand how the web works
  2. 2Build a frontend with HTML and CSS
  3. 3Add interactivity with JavaScript
  4. 4Connect a backend and serve data
  5. 5Deploy your app to a real URL
Skills

Ship AI Products in Production

The anchor milestone. Six verified steps from your first API call to a multi-agent system with a real evaluation suite.

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6 milestones

  1. 1Set up your AI development environment
  2. 2Ship your first LLM feature publicly
  3. 3Build a RAG system with citations
  4. 4Use function calling with two tools
  5. 5Build and evaluate a multi-step agent
  6. 6Ship an AI product with 10 users
Skills

Ship an AI Product to Real Users

Shipping to 10 real users is the proof that you can build something people actually want — not just something that works in a demo.

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3 milestones

  1. 1Deploy a working prototype to a URL
  2. 2Get five real users and their feedback
  3. 3Prove 10 users return over 4 weeks
SkillsOptional

Make Your First Open Source Contribution

Optional but strongly recommended. OSS contributions build your professional reputation and force you to read production-quality code at scale.

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3 milestones

  1. 1Understand a project's codebase
  2. 2Open your first pull request
  3. 3Get your pull request merged

Resources for this path

No affiliate links. No sponsored placements.

The best free practical deep learning course. Starts with working code, explains the theory later. Jeremy Howard's top-down approach is the fastest path from zero to shipping.

Harvard's free Python course. Rigorous, well-paced, and forces you to solve real problems. Better than any tutorial series for building genuine Python fluency.

MIT's free course on the dev tools every engineer uses daily — shell, git, editors, debugging. Most courses skip this entirely. Do not.

Free full-stack curriculum with certificates. Use it to fill specific gaps — the JavaScript, Python, and data structures sections are particularly good.

The best free neural network course on YouTube. Karpathy builds everything from scratch — watching someone who understands it deeply do it slowly is irreplaceable.

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