AI developers are among the most in-demand — and most misrepresented — professionals in the technology market right now. The gap between someone who has used ChatGPT and someone who can build a production AI system is enormous. These 7 questions will help you find the real thing.
Why Standard Hiring Practices Fail for AI Roles
A candidate’s CV can list “experience with GPT-4, LangChain, and vector databases” while having only surface-level familiarity with each. Traditional interviews that ask about algorithms and data structures don’t reveal whether someone can architect, deploy, and maintain a production AI system. You need AI-specific evaluation criteria.
7 Questions to Ask Every AI Developer Candidate
1. “Walk me through an AI system you’ve put into production. What was the hardest part?”
Production experience is everything. If they’ve only built hobby projects or demo apps, you’ll discover it quickly. Listen for specifics: latency issues, hallucination mitigation, cost management at scale, monitoring and alerting.
2. “How do you handle hallucinations in an LLM-based product?”
Hallucinations are the central reliability challenge of LLM applications. Good answers include: retrieval-augmented generation (RAG), structured output with schema validation, human-in-the-loop for high-stakes decisions, confidence scoring, and comprehensive eval suites.
3. “Which model would you use for [specific use case], and why not the others?”
Model selection is a skill. Someone who defaults to GPT-4 for everything hasn’t thought hard enough about cost, latency, capability tradeoffs, and data residency requirements.
4. “How do you evaluate whether an AI feature is working correctly in production?”
Evals (evaluation frameworks) are the testing discipline of AI. Without them, you’re flying blind. Good candidates have opinions on LLM-as-judge, golden datasets, automated regression testing, and user feedback loops.
5. “How would you design a RAG system for this use case?”
RAG (Retrieval-Augmented Generation) is now a core AI engineering pattern. Ask for their approach to chunking, embedding models, vector database selection, query rewriting, and result reranking.
6. “What’s your approach to AI cost management at scale?”
API costs can spiral quickly. Good engineers think about caching, prompt engineering for efficiency, model selection by task complexity, and batching strategies.
7. “How do you handle data privacy when using third-party LLM APIs?”
GDPR, PDPA, and data residency requirements are real constraints. Good candidates know when to use local/open-source models, how to PII-scrub inputs, and which enterprise API agreements provide the necessary contractual protections.
Red Flags to Watch For
- Can’t explain their past projects without vague generalisations
- No mention of testing, evals, or monitoring
- Recommends the same LLM for every use case
- Has never managed LLM API costs in a production system
Alternative: Hire a Team Instead
For most businesses, hiring a single AI developer isn’t the right model. AI projects need ML engineers, prompt engineers, backend developers, and DevOps expertise working together. Engaging a specialist AI development company often delivers better results at lower risk than building an in-house team from scratch.
WavesItSolution provides dedicated AI engineering teams with production experience. Explore our Hire Developer options or discuss your requirements with us.