AI Engineer 101
A practical guide to becoming an AI engineer — from Python tooling to production LLM systems.
Why I Wrote This Series
I came to AI engineering from a software engineering background. I knew how to build APIs, write tests, deploy containers — but when I started building systems that used LLMs, embeddings, and vector search, I realized the gap between "software engineer who uses AI APIs" and "AI engineer who builds reliable AI systems" was bigger than I expected.
The hard part wasn't calling an API. It was knowing how tokens actually work so I could debug why my prompts were getting truncated. It was understanding embeddings well enough to know why my retrieval was returning irrelevant results. It was learning to evaluate AI outputs when there's no single "correct" answer.
This series documents what I learned building AI-powered systems in my own projects — a RAG service over my personal knowledge base, an LLM-powered DevOps agent, and various automation tools. Every code example runs. Every pattern comes from something I actually built and debugged.
No fake product scenarios. No "imagine you're building a chatbot for a Fortune 500 company." This is what AI engineering looks like when you're building it yourself.
How This Fits with Other Series
AI Engineer 101 (this)
The full picture — role, tooling, LLMs, embeddings, prompts, APIs, eval, production
Deep dive into building FastAPI services with Claude
End-to-end retrieval-augmented generation with pgvector
Building a single agent with ReAct, memory, and tool use
Tensors, autograd, neural networks from scratch
Using pre-trained models and fine-tuning
Read this series first if you're transitioning from software engineering to AI engineering. The other series go deeper on specific topics.
What You Will Learn
Part 1: What is an AI Engineer?
The AI engineer role — how it differs from ML engineer and data scientist
The skills map: software engineering + AI fundamentals + system thinking
Where AI engineers fit in a team
My path from DevOps/backend engineering to AI engineering
Part 2: Python Tooling for AI Engineers
Why Python dominates AI engineering (and where it doesn't)
Project structure with
pyproject.toml,uv, and virtual environmentsThe essential library stack: FastAPI, Pydantic, httpx, sentence-transformers
Setting up a reproducible AI development environment
Part 3: How LLMs Work — What AI Engineers Need to Know
Tokens, context windows, and why they matter for your code
The transformer architecture in plain language
Temperature, top-p, and sampling — what the parameters actually do
Local models vs API models — when to use which
Part 4: Embeddings and Vector Search
What embeddings actually represent and why they're useful
Generating embeddings with sentence-transformers and API providers
Vector similarity: cosine, dot product, Euclidean distance
Storing and querying vectors with pgvector
Part 5: Prompt Engineering for Production Systems
Why prompt engineering is software engineering, not guesswork
System prompts, few-shot examples, and structured output
Prompt templates with Pydantic validation
Defensive prompting: handling edge cases and adversarial input
Part 6: Building AI-Powered APIs with FastAPI
Designing REST endpoints for LLM-backed services
Streaming responses with Server-Sent Events
Async patterns for concurrent LLM calls
Rate limiting, retry logic, and cost control
Part 7: Evaluating and Testing AI Systems
Why traditional testing doesn't work for AI outputs
Building evaluation datasets from your own usage
Automated evaluation with LLM-as-judge
Regression testing when you change models or prompts
Part 8: AI Engineering in Production
Observability for LLM systems: latency, token usage, cost tracking
Guardrails and content filtering
Caching strategies for embedding and LLM responses
When to use a framework vs building from scratch
Prerequisites
Python 3.11+ (we use 3.12 features throughout)
Familiarity with FastAPI and async/await
A GitHub Models API key or OpenAI/Anthropic API key
Basic understanding of REST APIs and JSON
If you've read Python 101 and REST API 101, you're ready.
Stack
Language
Python 3.12
API Framework
FastAPI
Data Validation
Pydantic v2
HTTP Client
httpx
Embeddings
sentence-transformers / GitHub Models API
Vector Store
PostgreSQL 16 + pgvector
LLM Providers
GitHub Models API, OpenAI, Anthropic
Testing
pytest + pytest-asyncio
Package Management
uv
Series Structure
Phase 1 — Foundations
Phase 2 — Core AI Concepts
Phase 3 — Building AI Systems
Phase 4 — Production Readiness
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