How to Tell Whether an AI Candidate Has Built Something That Works

An AI engineer’s CV says they built a chatbot, deployed a model and improved accuracy. The projects sound impressive. But what did the candidate personally do—and did the work solve a real problem?

As more companies hire for AI roles, those questions matter. A polished demo can show what a system does under ideal conditions. The job may require someone who can make it useful, reliable and maintainable when real users arrive.

Start with the Problem, Not the Tools

Before assessing candidates, the hiring team should define what the role will deliver. Is the person developing a prototype, improving an existing product or taking a system into production?

The answer changes the skills required. Someone who is excellent at experimenting with models may not have experience monitoring a live system. Another candidate may be particularly strong at integrating AI into a product that customers use every day.

A clear brief helps interviewers ask about the right kind of work.

Ask What Happened After the Demo

A useful interview question is: “What happened when people started using it?”

Ask the candidate how they measured whether the system helped. What mistakes did it make? What feedback came from users? How did the team respond when the output was wrong or the system became slow or expensive to run?

Strong candidates should be able to discuss trade-offs as well as successes. They may describe a feature they simplified, an approach they abandoned or a manual check they kept because automation was not reliable enough. These details often reveal more than a list of model names.

Find Out What the Candidate Owned

AI projects are usually team efforts. One person may prepare data, another may develop the model, and others may handle integration, testing and deployment. That is normal. The interview should establish which decisions the candidate made and how they worked with the rest of the team.

Ask them to walk through one project from beginning to end. Where did their responsibility start and end? Which difficult decision did they make? What would they do differently now?

The goal is to understand their contribution, not to expect them to have built an entire system alone.

Use a Realistic Assessment

A short work discussion can be more useful than a generic technical test. Present a problem similar to one the company faces, along with limited information. Ask how the candidate would clarify the requirement, evaluate possible approaches and decide what to measure.

Pay attention to the questions they ask. Candidates who first want to understand users, data and the cost of errors may be better prepared for the work than those who immediately suggest a complex model.

At SilverPeople, we help companies define AI roles around the results they need, then assess candidates’ relevant work and decisions. That makes it easier to look beyond familiar keywords on a CV.

The best evidence of AI capability is not a demo alone. It is a clear account of what the candidate built, how it performed and what they learned when people used it.