This guide comes from hundreds of conversations our AI recruitment specialist, Brad, has had with hiring managers across the tech industry. From CTOs and engineering leaders to founders building their first technical teams, he hears first-hand what makes a candidate stand out, what hiring managers remember after an interview and what ultimately influences their decision.

We asked Brad to bring those conversations together into five practical things every candidate can use to give themselves the best possible chance in their next interview.

If you’re applying for a role in AI or technology at the moment, it can be difficult to know what actually makes the difference.

You can have the right technologies on your CV, experience with the right platforms and a background that looks almost identical to the job description, yet still find yourself competing against several other people who can say exactly the same thing.

I spend a lot of time speaking to both sides of that process. Candidates tell me what they’re worried about before an interview, then hiring managers tell me what stood out afterwards. What’s interesting is that the things candidates often focus on aren't always the things that ultimately influence the decision.

Technical capability obviously matters. If you’re interviewing for an AI Engineering position, you need to understand AI Engineering. Increasingly, though, hiring managers are trying to understand what you can do with that knowledge.

So, if I was interviewing for a technology role tomorrow, these are the five things I’d make sure came across.

1. Evidence that you've actually built something

There’s a big difference between knowing a technology and having used it to solve a real problem.

That distinction has become particularly important in AI. It’s relatively easy to add LLMs, RAG, agents or a particular framework to a CV. Hiring managers want to understand what happened when you actually tried to use those things.

Perhaps you built an internal AI tool that saved your team several hours every week. Maybe you redesigned a data pipeline that significantly improved processing time. Perhaps you introduced AI-assisted development into your workflow and helped your team ship a feature faster.

The interesting part is the story behind it. What problem were you solving? Why did you choose that approach? What did you personally own? What worked? What did you learn?

That tells someone far more about you than a list of technologies ever will.

-The takeaway-

Before your interview, choose three projects you're genuinely proud of and prepare a simple explanation for each:

The problem → Your decision → What you built → The result → What you learned

Keep it concise enough that you can explain each one naturally in two or three minutes. Wherever possible, include an outcome: time saved, revenue generated, latency reduced, users reached, costs lowered or another meaningful measure.

You're giving the interviewer evidence of what happens when they put a problem in front of you.

2. The ability to explain why you made a decision

This is probably one of the most underrated interview skills in technology.

Hiring managers aren't only interested in whether your solution worked. They want to understand how you arrived at it.

Why did you use that model? Why that architecture? Why build something internally instead of buying it? What alternatives did you consider? What trade-offs did you make?

There isn't always a perfect technical answer, particularly when you're working with rapidly evolving AI technologies. Strong engineers understand that and can explain the reasoning behind a decision rather than presenting every choice as obvious in hindsight.

This is where judgement starts to become visible.

-The takeaway-

When preparing examples for an interview, add one extra question to every project:

“Why did I do it this way?”

Then prepare yourself for the follow-up:

“What would you do differently now?”

Being able to answer both confidently shows that you can evaluate your own decisions, learn from experience and think beyond the code itself.

3. Proof that you can work with AI, rather than simply talk about it

AI fluency is becoming relevant across a much broader range of technology roles. That doesn't mean every candidate needs to be an AI Engineer.

What matters is understanding how these tools fit into your work.

For a software engineer, that might mean explaining how you use coding agents while maintaining review standards. For a product person, it could mean demonstrating how AI has changed research, prototyping or product discovery. For someone working in data, it might be how you're using AI to improve analysis or automate parts of your workflow.

Hiring managers are increasingly interested in how candidates combine their existing expertise with these new capabilities.

Simply saying you “use ChatGPT” doesn't reveal very much. Explaining how you've redesigned a workflow around AI does.

-The takeaway-

Audit your own working week and find one practical example of AI making you better at your job.

Be ready to explain the tool you used, what you gave it responsibility for, what you kept responsibility for yourself and what changed as a result.

That final part is particularly useful. It demonstrates that you understand AI as a working tool rather than a CV keyword.

4. An understanding of the business behind the technology

One of the strongest candidates I can put in front of a client is someone who understands that technology exists to achieve something.

It could be increasing revenue, improving a customer experience, reducing costs, making a process faster or enabling a company to launch a completely new product.

You don't need an MBA to demonstrate commercial awareness. You need to be curious about why the work matters.

The engineers who can connect technical decisions to business outcomes tend to have much richer conversations with CTOs and hiring managers because they're speaking about the same problem from two different perspectives.

This becomes increasingly important as you move into senior positions. At Staff, Principal or leadership level, the conversation naturally expands beyond what you can build towards what the organisation should build and why.

-The takeaway-

Before any interview, spend 20 minutes researching three things:

How does this company make money?
Who uses its product?
What is the company trying to achieve next?

Then look at the role again.

Try to understand why they need this person now.

That gives you a much better foundation for the interview and allows you to ask far more interesting questions when you get the opportunity.

5. Someone they'd actually want on the team

This one sounds obvious, but I think candidates sometimes underestimate it.

Hiring managers are building teams, rather than collecting CVs.

They want technically capable people, but they're also thinking about what it will be like to solve difficult problems with you every day.

Can you explain complicated ideas clearly? Do you listen? Can you disagree constructively? Are you interested in other people's ideas? Can you take feedback? Do you help people around you improve?

These qualities are becoming more valuable as engineering itself becomes increasingly collaborative. AI may be able to generate more of the work, but humans still need to agree what should be built, review decisions, understand customers and work through the difficult parts together.

You don't demonstrate that by telling an interviewer you're a “great communicator”. You demonstrate it through the conversation itself.

-The takeaway-

Treat the interview like a discussion rather than an exam.

Listen properly to the question. Ask for clarification when you need it. Explain your thinking rather than racing towards an answer, and be comfortable saying when you don't know something.

Most importantly, have good questions of your own.

Ask what the team is trying to achieve. Ask what their strongest engineers do particularly well. Ask what success looks like after six months. Ask what technical challenge they haven't solved yet.

A genuinely curious candidate is memorable.

One final thought…

The technology hiring market is becoming increasingly skills-focused, which I think is ultimately a positive thing for candidates. Your previous job title, university or the logos on your CV can provide context, but they don't tell the whole story.

What you can do, how you think, how you work with other people and the impact you've already created give hiring managers something much more useful.

So before your next interview, I'd spend less time trying to predict every technical question you might be asked and more time thinking about the evidence you want the interviewer to remember when the call ends.

If they come away knowing what you've built, how you make decisions, how you're using AI, how you think about their business and what you'd be like to work alongside, you've given them five very good reasons to want another conversation.