| 0 | How to Use This Guide | 3 |
| 1 | What an Applied AI Engineer Actually Does | 4 |
| 2 | Competency Map — What to Learn and How Deep | 6 |
| 3 | Phase 1 — Python & Backend Foundation | 8 |
| 4 | Phase 2 — AI, ML, Transformer & LLM Foundations | 12 |
| 5 | Phase 3 — Model APIs, Prompting & Structured Outputs | 18 |
| 6 | Phase 4 — Embeddings, Search & Vector Databases | 23 |
| 7 | Phase 5 — Retrieval-Augmented Generation (RAG) | 28 |
| 8 | Phase 6 — Tool Calling & AI Workflows | 35 |
| 9 | Phase 7 — Agents & LangGraph | 40 |
| 10 | Phase 8 — Model Context Protocol (MCP) | 46 |
| 11 | Phase 9 — Evaluation, Testing & Observability | 50 |
| 12 | Phase 10 — AI Security & Safety Engineering | 55 |
| 13 | Phase 11 — Production Engineering, Cloud & LLMOps | 60 |
| 14 | Phase 12 — Multimodal AI, Fine-Tuning & Open Models | 65 |
| 15 | The Complete 24-Week Study Plan | 69 |
| 16 | Portfolio Projects: Beginner to Flagship | 72 |
| 17 | Production Architecture Reference | 76 |
| 18 | Interview & Job Preparation | 78 |
| 19 | What NOT to Waste Time On | 82 |
| 20 | Master Checklist | 84 |
| 21 | Verified Learning Resources | 86 |
| 22 | Next Steps After the 24 Weeks | 88 |
| 23 | Final Revision Guide | 90 |
| 24 | Cheat Sheet | 93 |
| 25 | Glossary | 95 |
| 26 | Final Practice Questions (with Answers) | 98 |
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