This is not a “learn to code” roadmap. It assumes your engineering fundamentals are solid (Node, Postgres, AWS, Docker, queues) and focuses on the AI-specific layer + the judgment that makes you hireable as an AI engineer.
In 2026, an AI engineer builds applications using pre-trained models — orchestrating LLMs, building reliable systems around them, and shipping features people actually use. You are not training models or writing papers. The work is production software engineering with an LLM at the core.
Three rules that will save you months
Your unfair advantage
The “production layer” — API design, DB, retries, queues, deployment, cost/latency thinking — is exactly where AI-course beginners drown, and you already have it. Don’t relearn it; reuse it.
The clever part of your plan
Your LeetCode clone is the perfect host for all of this. By Phase 4 you’ll have, in one repo: a real backend system + a structured-output feature + a pgvector RAG feature + a tool-using agent + an eval harness + self-hosted observability — all demoable and explainable in depth. That single repo is a stronger AI-engineering portfolio than a dozen disconnected tutorial clones.
How to actually finish this (so it doesn’t stall)
- One shipped project per phase, in public. No project = phase not done.
- Build raw before frameworks, every time.
- Timebox: ~10–12 weeks gets you genuinely employable as an AI engineer given your base. Going deep takes longer — that’s fine.