The AI Engineer Roadmap for 2026: Why Steps 1-2 Determine Everything

Everyone's asking how to become an AI Engineer.

Most of the advice is backwards.

"Start with LangChain!"
"Learn vector databases!"
"Build agents!"

These are Step 4-5. Not Step 1.

Here's the actual path.

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The 6 Steps

1. Python Foundations ← Most skip this
2. AI System Design ← Most ignore this
3. Production Backends
4. RAG Systems
5. Monitoring
6. Deployment

Steps 1-2 determine everything that follows.

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Step 1: Python Foundations

Not "hello world."

Real software engineering:

✅ Proper development environment
✅ Virtual environments for every project
✅ Git workflows
✅ Testing with pytest
✅ Error handling
✅ Logging
✅ Environment variables

Why it matters: AI Engineering is software engineering with AI components.

Can't structure a Python project? Can't structure an AI system.

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Step 2: AI System Design

The key principle:

Use as little AI as possible.

LLMs:

• Expensive
• Slow
• Non-deterministic
• Hallucinate

Python:

• Free
• Fast
• Deterministic
• Reliable

Goal: use AI only where necessary.

Typical architecture:

User Request
    ↓
Input Validation (Python)
    ↓
Routing (Python)
    ↓
Context Building (Python + DB)
    ↓
LLM Call (ONLY when needed)
    ↓
Output Validation (Python)
    ↓
Response (Python)

One LLM call. Six Python steps.

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The Book

110 chapters covering this path:

• Ch 1-59: Python foundations
• Ch 60-80: LLM integration
• Ch 81-91: RAG, agents
• Ch 92-98: AI design patterns
• Ch 99-110: Deployment + projects

Writing it now. Free chapters on Substack: https://substack.com/@samuelochaba