Multiple surveys show that in less than two years, generative AI-tools have moved from early experimentation to mainstream. Every venture-backed company now wants engineers who can prompt GPT-4, fine-tune open-source models, and wire an agent to outside APIs. Supply has not caught up, so founders must compete for talent that did not exist on resumes five years ago.

Tool mastery beats puzzle solving

Large language models can generate scaffolding code in seconds. A strong AI engineer will:

• Feed precise prompts, inspect output with a critical eye, and patch errors quickly.

• Break large goals into small, machine-readable steps.

• Combine API calls, vector stores, and safety checks into a stable product.

A candidate who handles a real bug fix in a shared IDE with ChatGPT open is more valuable than one who can invert a binary tree from memory.

Judgment is the new secret sauce

LLMs often sound confident, even when they're wrong. Skilled engineers know how to spot when something feels off, test it quickly, and adjust without fuss. In interviews, look for thought processes like: “I’d check how the model handles edge cases, track every response, and add guardrails if errors go over 3%.”

Product intuition matters

Fine-tuning a model is wasted effort if the output feels clunky to users. Ask how a candidate aligned tone, latency, and privacy with customer needs. If they mention hooking Slack alerts to catch spicy language before it hits production, move them forward.