30-Day AI Engineer Learning Path

A structured roadmap from AI/ML fundamentals to building production-ready AI systems.

This roadmap is divided into 6 phases, with 5 lessons per phase. Each phase builds on the previous one and gradually moves from foundational concepts to real-world AI engineering.

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Phase 1 — AI & ML Foundations

The first phase establishes the core knowledge required to understand how AI systems are built.

1. AI Engineering Fundamentals
2. Python for AI Engineering
3. Machine Learning Fundamentals
4. Data Preparation & Feature Engineering
5. Model Training & Evaluation

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Phase 2 — Deep Learning & LLM Foundations

This phase introduces the concepts behind modern deep learning and Large Language Models.

1. Neural Networks Fundamentals
2. Deep Learning & Training
3. What Is an LLM?
4. Tokens & Tokenization
5. Transformers & Attention

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Phase 3 — LLM Engineering

This phase focuses on building applications using modern Large Language Models.

1. Embeddings & Vector Representations
2. LLM APIs & Inference
3. Prompt Engineering
4. Structured Outputs & Schema Validation
5. Function Calling & Tool Use

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Phase 4 — RAG & Knowledge Systems

This phase covers how AI applications can retrieve and use external knowledge.

1. Vector Databases & Similarity Search
2. RAG Fundamentals
3. Document Ingestion & Chunking
4. Advanced RAG & Retrieval
5. RAG Evaluation & Hallucination Reduction

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Phase 5 — AI Agents

This phase introduces agentic systems that can reason, use tools, maintain state, and execute multi-step workflows.

1. AI Agents Fundamentals
2. Agent Architecture & Loops
3. Agent Memory & State
4. Multi-Agent Systems
5. Human-in-the-Loop AI

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Phase 6 — Production AI Engineering

The final phase focuses on taking AI systems from prototypes to reliable production systems.

1. AI Evaluation & Testing
2. AI Security & Guardrails
3. AI Observability, Performance & Cost
4. Production AI System Design
5. End-to-End Production AI Project

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Learning Progression

flowchart TD
    A["Phase 1<br/>AI & ML Foundations"]
    B["Phase 2<br/>Deep Learning & LLM Foundations"]
    C["Phase 3<br/>LLM Engineering"]
    D["Phase 4<br/>RAG & Knowledge Systems"]
    E["Phase 5<br/>AI Agents"]
    F["Phase 6<br/>Production AI Engineering"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F

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The Goal

Understand AI
      ↓
Understand ML
      ↓
Understand Deep Learning
      ↓
Understand LLMs
      ↓
Build LLM Applications
      ↓
Build RAG Systems
      ↓
Build AI Agents
      ↓
Evaluate & Secure AI
      ↓
Optimize AI Systems
      ↓
Design Production AI

The documentation for each lesson will explain the concepts in depth, with practical examples, architecture diagrams, code, trade-offs, and real-world use cases.