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🤖 AI Engineering Roadmap
A 6-phase path to become an AI engineer — built on your existing backend skills, hosted on your LeetCode clone.
Goal
Ship raw LLM APIs → RAG on pgvector → a tool-using agent → evals & observability, one public project per phase. 01
The framing (read this first)
⏱ 10m 02 What an AI engineer actually does in 2026, the three rules that save months, and why your backend skills are an unfair advantage.
Phase 0 — Position yourself
⏱ 12m 03 ≈1 week. Reuse the production skills you already have; close the one real gap (enough Python to read notebooks).
Phase 1 — LLM APIs & Prompting Engineering
medium ⏱ 35m 04 Call LLM APIs correctly, treat tokens as money, implement streaming, and make structured outputs reliable with tool use.
Phase 2 — Embeddings, Vector Search & RAG
hard ⏱ 40m 05 Build a production RAG pipeline — chunking, embedding, pgvector retrieval, reranking, and the quality levers that actually matter.
Phase 3 — Tool Use, Agents & the ReAct Loop
hard ⏱ 40m 06 Build production agents — tool use patterns, the ReAct loop, parallel tool calls, error recovery, and when to use deterministic workflows instead.
Phase 4 — Evals, Observability & Production
hard ⏱ 38m 07 Ship AI to production — evaluation frameworks, LLM-as-judge, tracing, latency optimization, cost dashboards, and A/B testing prompts.
Phase 5 — Fine-Tuning, Specialize & Ship
hard ⏱ 35m 08 Know when fine-tuning beats RAG, implement LoRA with real code, pick a specialization depth, and ship one polished AI product publicly.
Stack cheat-sheet (2026)
⏱ 8m Your default pick per layer — and the alternatives worth knowing — for the current AI-engineering stack.