How to Become an AI Engineer (Roadmap 2026)

1Months 1–2: Python, maths basics, data handling.

This section provides key insights and practical guidance.

2Months 3–4: Machine learning fundamentals and projects.

This section provides key insights and practical guidance.

3Months 5–6: Deep learning.

This section provides key insights and practical guidance.

4Months 7–9: LLMs, APIs, RAG, and a deployed project.

This section provides key insights and practical guidance.

5Stage 2: Machine Learning

Learn the core ML concepts: how models learn from data, training vs testing, common algorithms, and

how to evaluate results honestly. Build a few small models with a library like scikit-learn to make it

6Stage 3: Deep Learning

Move to neural networks — the engine behind modern AI. Understand how they work, and get hands-on

with a deep-learning framework. This stage unlocks vision, language, and generative AI.

7Stage 4: Working With LLMs and APIs

This is where modern AI engineering increasingly lives. Learn to build with large language models:

calling AI APIs, prompt engineering, retrieval-augmented generation (RAG), and building agents and

• Skipping fundamentals to jump straight to LLMs.
• Endless courses, no projects — build as you go.
• Ignoring deployment — shipping is a core skill.
• Collecting certs with nothing built to show.
• Path: foundations → ML → deep learning → LLMs/APIs → deployment.