AI hiring guide: from job post to signed offer

1. Write job posts that name the stack

Replace vague asks like “familiarity with AI” with tools and constraints: languages, frameworks, model providers, data sources, and what “done” means. Add workplace type, location, and employment model early so candidates self-filter.

• Must-have vs nice-to-have — separate non-negotiable skills from bonus experience.
• Outcomes — ship a copilot, cut ticket volume, improve eval scores—pick one primary outcome.
• Safety and compliance — if regulated data or PII matters, say so up front.

2. Screen for shipped work, not buzzwords

Ask for artifacts: repos (when possible), write-ups, metrics, or demo flows. Short take-home prompts tied to your real stack beat generic Leetcode for many applied-AI roles—if you use them, keep scope respectful of candidate time.

3. Align interviews with the job

If the role is mostly integration and product judgment, weight system design and tradeoffs over pure model math. If research depth matters, include eval methodology and failure analysis. Consistency across interviewers beats one hero panel.

4. For candidates: mirror the post

Tailor your pitch to the tools and outcomes in the listing. Link or describe one project that matches their problem shape. If you lack exact stack overlap, show adjacent proof and a clear learning plan.

Further reading on Ganloss

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