AI Engineering Overview

What Is AI Engineering?

AI Engineering is the practice of building, deploying, and maintaining AI-powered systems in production — combining software engineering with machine learning, LLMs, and data pipelines.

This is not just theory. AI Engineering means writing real code that uses AI models to solve real problems — APIs, agents, RAG systems, embeddings, vector databases, and more.

The Learning Path

This curriculum takes you from zero to production-ready AI engineer across 11 phases:

Phase	Topic	What You’ll Learn
1	AI Fundamentals	What AI is, how it works, terminology
2	Machine Learning	Supervised/unsupervised learning, algorithms
3	Deep Learning	Neural networks, CNNs, RNNs
4	Large Language Models	How LLMs work, tokenization, transformers
5	Retrieval Systems & RAG	Embeddings, vector databases, retrieval-augmented generation
6	AI Agents	Autonomous agents, tool use, planning
7	Model Context Protocol	MCP architecture, servers, clients
8	AI Frameworks & Ecosystem	LangChain, LlamaIndex, LangGraph, CrewAI, DSPy
9	Production AI Engineering	LLMOps, deployment, monitoring, evaluation, security, cost optimization
10	Real-World AI Projects	Build ChatGPT, Perplexity, Cursor, Copilot, enterprise AI platform
11	AI Interview Preparation	Career guide, mock interviews, system design, portfolio building

What You Can Build After Phase 10

✓ Complete ChatGPT clone with streaming and multi-model support ✓ Perplexity-like AI search engine with citations ✓ NotebookLM-style document research assistant ✓ Cursor-like AI-first code editor ✓ GitHub Copilot-style code completion ✓ AI code reviewer with automated PR comments ✓ Enterprise AI platform with RAG, agents, MCP, and monitoring

Prerequisites

No AI background needed. Helpful to have:

Basic programming knowledge (any language)
Curiosity about how intelligent systems work