10 Steps to Building a Better AI Recruitment Process

Hiring AI talent has never been more competitive.

Whether you’re looking for an AI Engineer, Machine Learning Engineer, Data Scientist or Head of AI, you’re competing for a relatively small pool of highly skilled professionals. The best candidates are often approached by multiple employers each week, meaning a slow or poorly planned recruitment process can quickly lead to missed opportunities.

Over the past 20 years, we’ve helped businesses hire technology professionals across the UK, from start-ups making their first AI hire to established organisations building entire AI functions. While every business is different, one thing remains consistent: companies with a clear, efficient recruitment process consistently secure better talent.

Here are ten practical ways to improve your AI recruitment process.

1. Define the Business Problem Before the Job Title

One of the biggest mistakes we see is businesses deciding they need an “AI Engineer” before they’ve identified what they actually need to achieve.

Are you building a recommendation engine? Automating internal processes? Developing computer vision models? Deploying generative AI tools across the business?

The answer determines the type of specialist you need.

For example, a Machine Learning Engineer focused on deploying models into production has a very different skill set to an NLP Engineer building large language model applications.

Getting this right at the start avoids attracting unsuitable candidates and saves weeks of wasted interviews.

Recruiter Insight

“The best hiring campaigns begin with the business challenge, not the job title. Once you understand the outcome you’re trying to achieve, identifying the right person becomes much easier.”

David Berwick, Director, Adria Solutions

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2. Stop Asking for Every Skill Under the Sun

AI is evolving too quickly for anyone to know everything.

Yet many job descriptions still ask for Python, TensorFlow, PyTorch, Azure, AWS, GCP, MLOps, Kubernetes, SQL, NLP, Computer Vision, prompt engineering and five years’ experience with technologies that barely existed five years ago.

Strong candidates often tell us they don’t apply because the requirements feel unrealistic.

Instead, separate your requirements into:

• Essential skills
• Skills that can be learned
• Nice-to-have experience

You’ll almost always attract a stronger and more diverse shortlist.

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3. Benchmark the Market Before You Recruit

Many recruitment campaigns fail before they’re even launched.

We’ve seen businesses budgeting £65,000 for an AI Engineer while competing against organisations offering £85,000 plus flexible working, private healthcare and dedicated learning budgets.

Candidates aren’t rejecting the role because they dislike the company. They’re simply comparing opportunities.

Before advertising, benchmark:

• Salary expectations
• Remote and hybrid working policies
• Benefits
• Notice periods
• Competitor demand

A small adjustment before the role goes live is far easier than restarting the recruitment process a month later.

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4. Sell the Opportunity, Not Just the Role

AI professionals want to know more than their responsibilities.

They want to understand:

• What problems they’ll solve
• How mature your AI strategy is
• Who they’ll work with
• Whether leadership genuinely supports AI investment
• How they’ll develop over the next few years

A generic job description listing technical skills won’t answer those questions.

The businesses that attract the best candidates explain why their projects matter.

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5. Don’t Expect the Best Candidates to Apply

One of the biggest misconceptions in AI recruitment is that posting a vacancy will bring the strongest candidates to you.

In reality, many of the people we place aren’t actively job hunting.

They’re delivering AI projects, leading engineering teams or developing products elsewhere.

Finding them requires proactive search, referrals, networking and targeted outreach rather than simply waiting for applications.

Recruiter Insight

“The strongest AI candidates rarely spend months applying for jobs. More often than not, they’re persuaded by an opportunity they hadn’t been looking for.”

David Berwick

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6. Move Faster Than Your Competition

We’ve seen candidates receive two or three interview requests within a single week.

If your process involves five interview stages spread across six weeks, there’s a good chance your preferred candidate will accept another offer first.

Aim to:

• Schedule interviews within a few days.
• Provide feedback within 48 hours.
• Reduce unnecessary approval stages.
• Keep candidates updated throughout.

Speed doesn’t mean rushing decisions. It means respecting candidates’ time.

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7. Make Technical Assessments Relevant

Nobody enjoys spending an entire weekend completing a coding exercise with no guarantee of feedback.

Instead, assess candidates using methods that reflect the role.

For example:

• Review previous AI projects.
• Discuss technical decisions they’ve made.
• Explore how they’d solve a real business challenge.
• Use a short practical exercise during the interview.

These conversations often reveal far more than a lengthy take-home test.

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8. Hire for Curiosity as Well as Technical Ability

The AI landscape changes almost monthly.

The frameworks your team uses today may not be the ones they’re using two years from now.

That’s why curiosity, adaptability and problem-solving often matter more than someone knowing every latest tool.

Ask candidates how they’ve kept their skills current, what they’ve learned recently and how they’ve approached unfamiliar challenges.

The best AI professionals never stop learning.

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9. Be Ready to Make an Offer

We’ve seen businesses lose outstanding candidates because internal approvals took another two weeks after the final interview.

Before interviewing anyone, agree:

• Salary range
• Benefits package
• Decision makers
• Start date
• Approval process

Preparation allows you to move confidently when you’ve found the right person.

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10. Keep Candidates Engaged Until Day One

Accepting an offer doesn’t guarantee someone will start.

A lengthy notice period gives competitors plenty of time to make another approach.

Simple actions make a huge difference:

• Check in every couple of weeks.
• Introduce them to future colleagues.
• Share updates about the business.
• Confirm onboarding plans early.

A candidate who already feels part of the team is far less likely to accept a counter-offer.

Final Thoughts

A successful AI recruitment process isn’t about adding more interview stages or longer technical tests.

It’s about creating a hiring experience that’s clear, efficient and built around today’s market.

The businesses making the strongest AI hires aren’t always the ones offering the highest salaries. They’re the ones that understand what they’re looking for, communicate well and move decisively.

As David Berwick puts it:

“Good AI recruitment isn’t about filling vacancies. It’s about finding people who’ll still be adding value to your business three or four years from now. That starts with getting the recruitment process right from day one.”

If you’re struggling to attract specialist AI talent, reviewing your recruitment process is often the quickest way to improve both the quality of candidates and your overall hiring success.