Path to Becoming
Become an AI Engineer
[ From fundamentals to building and deploying models that solve real problems. ]
The Architecture of a AI Engineer
An AI engineer turns data and models into working systems. This path takes you from the core mathematics and programming behind machine learning to building, evaluating, and deploying AI systems — verified by execution, not by watching.
The Verification Standard
To complete this path, you must train, evaluate, and deploy a machine learning system end-to-end — including a generative AI feature — verified by automated tests and live evaluation.
The Curriculum Map
1
Phase I: The Foundation
Weeks 1-4The math and code behind every model. No frameworks before fundamentals.
Linear Algebra & Calculus
Probability & Statistics
Python & Data Structures
Applied Programming
2
Phase II: Machine Learning
Weeks 5-8From predictions to models. Understanding how machines actually learn from data.
Supervised & Unsupervised Learning
Model Evaluation
Neural Networks
Feature Engineering
3
Phase III: Applied AI
Weeks 9-12Building systems, not notebooks. Shipping AI that performs in production.
Deep Learning Architectures
LLM & Generative AI Systems
MLOps & Deployment
Evaluation & Guardrails