AI Engineering & Leadership Roadmap

A 9-Month Path from Engineer to AI Engineering Leader

Everyone tells you to "learn AI." Almost nobody tells you what to study, in what order, for how long, or how you'll know you're actually making progress. This roadmap fixes that.

This is a complete, structured system — not another list of scattered YouTube links. Over 39 weeks, you move from ML foundations through LLM engineering, RAG, AI agents, LLMOps, fine-tuning, and multimodal systems, finishing with a capstone platform and an executive-ready AI business case. Every week has a clear topic. Every month ends with a real, working build. Every hour is tracked, so you always know if you're on pace or falling behind.

What You Get

An interactive Excel tracker — set your own start date and all 39 weekly dates recalculate automatically. Log your Status, Actual Hours, and security checklist progress every week, with a live dashboard showing exactly how far along you are.

A polished PDF guide — the full syllabus at a glance, a phase-by-phase resource guide of curated (mostly free) courses and docs, and a column-by-column legend so you're never guessing what to fill in.

The 9-Month Journey

1. Foundations — supervised/unsupervised learning, embeddings, transformers
2. LLM Engineering — prompting, model selection, hallucination & reliability
3. RAG — chunking, vector search, reranking, end-to-end retrieval pipelines
4. Advanced RAG + Agents — context compression, agentic retrieval, orchestration basics
5. AI Agents — tool calling, memory, reflection, human-in-the-loop
6. Multi-Agent + LLMOps — manager/specialist patterns, tracing, evaluation, guardrails
7. LLMOps + Fine-Tuning — serving, monitoring, LoRA/QLoRA in a single compressed month
8. Multimodal Systems — vision, OCR, speech-to-text, applied to real customer-experience use cases
9. Capstone + Strategy — an enterprise agentic AI platform, plus the business case to sell it internally

Nine build projects ship along the way — a simple LLM app, an engineering assistant, a RAG-powered document intelligence tool, an incident investigation agent, a multi-agent system with observability, and more — so you finish with a portfolio, not just notes.

Built-in buffer weeks mean the plan bends when life happens, without you falling off track.

Who This Is For

Engineers and technical leads who want to move into AI engineering with a real plan instead of tab-hoarding fifty bookmarks. If you've been meaning to "get serious about AI" for months but keep starting and stopping, this gives you the structure to actually finish.

Why It Works

Most people don't fail at learning AI because the material is too hard — they fail because there's no plan, no pacing, and no way to see progress. This roadmap solves all three: a fixed weekly rhythm, an honest hours-logged tracker, and a shipped project every single month.

39 weeks. 252 hours. 9 shipped projects. One clear path.