Hiring for AI: What to Watch Out For

Almost every CV now mentions AI. Very few of those candidates have shipped an AI system into production. Knowing the difference is the most valuable hiring skill of the decade.

AI adoption is everywhere, but scaled AI capability is rare. Organisations racing to hire AI talent often end up with people who can talk about AI but cannot build or govern it.

This whitepaper explains the different AI roles, what real experience looks like, the red flags to watch for and how to structure an AI hiring process.

What is inside this free whitepaper

• The AI role map: from data engineer to AI product lead
• Five red flags in AI candidates
• Interview questions that reveal real experience
• Build, buy or borrow decisions for AI capability

Who should read it

CIOs, CTOs, heads of data and hiring managers building AI, machine learning or data capability.

Frequently asked questions

What AI roles should a company hire first?

Usually data engineers and an experienced AI or ML engineer who can take use cases to production, supported by governance from day one.

How do I know if an AI candidate has real experience?

Ask them to walk through a system they put into production: data, model choice, evaluation, deployment, monitoring and what went wrong.

Are AI skills in demand in Australia?

Yes. The World Economic Forum found two-thirds of employers plan to hire talent with specific AI skills.

Download Hiring for AI: What to Watch Out For free using the form. It is written for Australian employers hiring IT talent, from 14+ years of specialist IT recruitment.