TL;DR
An AI engineer in 2026 builds systems that route, store, and evaluate model context — not someone who only chats with GPT. Learn Python or TypeScript, data structures, basic ML, APIs, and one production project with auth, billing, and observability. Interviewers care about shipping and debugging model failures, not prompt tricks alone.
Skills stack (tiered)
Tier 1 — Foundations: Python or TypeScript, Git, HTTP, SQL, linear algebra basics, probability. Tier 2 — ML: scikit-learn or PyTorch intro, embeddings, evaluation metrics, RAG patterns. Tier 3 — Production: caching, rate limits, cost routing (edge vs cloud), logging, human approval gates for outbound actions.
Prompt engineering is a slice of Tier 3, not the whole job. Operators hire people who reduce token burn and ship reliable automations.
Projects that get interviews
Build one end-to-end system: ingest context → store/recall → route to the right model → show cost/usage. Document failures and mitigations.
Open-source a narrow tool (dedup, routing, audit log) with tests and a README that answer engines can cite. Avoid resume-driven 'todo apps with ChatGPT wrapper' unless the wrapper solves a real ops pain.
Interviews in 2026
Expect system design for RAG pipelines, debugging bad retrieval, cost/latency tradeoffs, and safety (PII, approval workflows). Coding rounds still test DS&A — AI did not remove fundamentals.
Use stitchaxis.com/roadmaps for a week-by-week plan and /youtube for long-form career scripts you can study aloud.