We spend a lot of time talking to CTOs, founders and engineering leaders about hiring. Yet, interestingly, the conversation rarely stays focused on recruitment for very long.

A company might initially come to us because it needs an AI Engineer, Data Engineer or another specialist hire. Once you start understanding what they’re building, where they’re going and what they need that person to achieve, the conversation becomes much broader.

You start talking about the product. The data behind it. How the engineering team is structured. Who owns particular decisions. What success looks like six or twelve months from now.

And over the first half of 2026, those conversations have reinforced something we’ve believed for a while: successful AI hiring is increasingly about the environment and team you build around people, rather than any individual hire.

Great hiring starts before the search

When businesses are growing quickly, recruitment can naturally become very reactive. A new project is approved, a capability gap appears and suddenly there’s a role that needs filling.

AI makes this particularly interesting because roles are evolving so quickly. What one business calls an AI Engineer can look completely different somewhere else. The skills required depend heavily on the product, the maturity of the technology and the team already in place.

That’s why some of the most productive conversations we have happen before a job description has even been written.

What does this person actually need to achieve? What will they own? Who will they work alongside? What capability already exists internally? And what does the team need to look like a year from now?

When those things are clear, finding the right person becomes much more straightforward. More importantly, the person joining has a much better opportunity to make the impact everyone expects of them.

An AI Engineer is part of the answer, rather than the whole answer

One of the clearest shifts we’ve seen is businesses thinking more carefully about the balance of their technology teams.

AI Engineers understandably receive a lot of attention. They're building some of the most exciting technology in the market and demand for genuinely experienced people remains significant.

But successful AI products involve much more than models.

They need good data. They need infrastructure capable of supporting them. They need people who understand the customer problem and can translate that into a product. They need engineers who can take something impressive in development and make it reliable in production.

It’s why a conversation that begins with “we need an AI Engineer” can sometimes end somewhere quite different.

Perhaps the next hire is actually a Data Engineer who can improve the foundations the AI team is working with. Maybe it’s a Product Manager who can bring greater clarity to what the team is building. In other cases, the priority is MLOps or platform capability that allows an existing AI team to get more into production.

There isn’t a universal template for an AI team, and there probably shouldn’t be. What we're seeing is greater thought going into how those different capabilities fit together.

For hiring managers, that makes workforce planning increasingly important. Instead of looking at vacancies individually, it becomes much more useful to think about the capability you're trying to build across the team.

The conversation then moves to what happens after someone joins

Perhaps the most interesting thing we’ve learned from speaking to engineering leaders is how much of successful hiring actually happens after recruitment has finished.

You can find an exceptional engineer and pay them an excellent salary, but their long-term impact will be shaped by the environment they walk into.

The strongest technology teams we work with tend to have clarity around what they’re building and enough freedom for talented people to work out how best to build it. Knowledge is shared naturally, people are encouraged to challenge ideas and engineers have opportunities to learn from the people around them.

That matters particularly in AI because everyone is learning.

The technology is developing at such a pace that even very experienced people are constantly encountering new tools, approaches and possibilities. Creating a culture where people can experiment and share what they discover gives that knowledge a chance to spread across the organisation rather than remaining with one individual.

It also changes the hiring proposition. Talented engineers talk to each other. A reputation for interesting work, strong leadership and a genuinely good engineering culture can become one of the most powerful attraction tools a company has.

So what does great AI hiring look like now?

For us, the biggest change is that the conversation has become more sophisticated.

Hiring managers are increasingly thinking about capability rather than headcount. They're looking at how Data, AI, Product, Platform and Engineering work together and thinking further ahead about what their teams will need as products mature.

That's a healthy evolution.

AI might be changing the technology businesses build, but many of the fundamentals behind great teams remain remarkably familiar. Give talented people an interesting problem, clear direction, good people to work alongside and an environment where they can continue developing, and you create the conditions for them to do exceptional work.

So when a business tells us it needs an AI Engineer, we'll obviously talk about the AI Engineer.

But we'll probably ask quite a few questions about everything around them too.

Because understanding the team you're trying to build is usually the best place to start.