The First Signal: “Wait, do we… need an AI engineer?”

The moment someone in your leadership team quietly asks, “Do we need an AI engineer?” you’re not just introducing a new role, you’re signaling a shift in how your company intends to operate going forward. This isn’t a decision driven by hype or because competitors are sprinkling “AI” into their pitch decks. It’s a recognition that the company has reached a threshold. A point where efficiency, prediction, and optimization can no longer be achieved through traditional tools alone. It’s that moment when you’ve hit a wall that only real computational intelligence can solve.

Companies that hire AI engineers for the right reasons do so because they’ve identified a strategic necessity. They’re buried in complex data, making high-stakes decisions, chasing operational efficiency, or trying to predict patterns their current tools weren’t built to handle. That’s when AI stops being a buzzword and becomes a core business function. Integrate it early, or prepare to trail behind the ones who do.

Scoping the Mission (aka Write a Sane Job Description)

Once you’ve accepted that AI is now part of your strategic future, the next step is where many companies stumble. Defining what you actually need. This is where the unicorn fantasy appears, the mythical candidate who can do it all. Build cutting-edge models, architect your infrastructure, clean your data, and possibly solve world peace before lunch.

Reality check: That person doesn’t exist.

Instead, you need to take an honest look at your internal needs and translate them into a focused job description. What are you solving, where are you heading, and what kind of person fits your culture? Too many teams chase someone who can “do it all” instead of clarifying what’s essential. Be realistic and specific, it keeps you grounded and prevents the “wish list” approach that just narrows your talent pool to zero.

Given how young the AI field is, the market is tight. Expecting someone with ten years of AI-specific experience is like waiting for a bus that doesn’t run. Your strongest candidates will likely be exceptional system developers with rock-solid fundamentals and the ability to learn and adapt quickly. But it’s their potential that will carry your AI work forward, not rigidly defined experience. Hire for potential, not perfection.

A limited market also means limited talent pools to dive into. So if you’re still fishing in your local pond, widen your net. Opening up to global remote talent expands your talent pool and lets you specialize more deeply across roles. It’s not just smart, it’s strategic.

Spotting Unicorns vs Good-Enough Horses

This is where things become surprisingly human. The difference between a competent hire and an exceptional one has less to do with their technical skill and more to do with who they are as people. Most candidates will tick the qualification boxes. That’s the easy part. What really matters is personality, initiative, and fit. How they think, what they are like, and how they’ll work alongside your existing team.

If someone’s already a strong system developer, they have the foundation to learn whatever new tools your AI setup demands. The real question is whether they’ll match your company’s rhythm, whether they’re proactive, self-driven, and ready to take ownership rather than wait for directions.

Proactivity is non-negotiable. So is cultural alignment. The candidate should move at your pace, share your expectations, and fit your internal dynamics. Don’t rely on CV glow alone, use a data-informed process. Personality and aptitude assessments reveal how someone actually thinks, collaborates, and learns. Done well, they help you spot the people who will elevate your team, not just blend into it.

At the end of the day, it’s not just about finding talent that can code. It’s about finding people who make your whole team better.

The Trial

Now, let’s talk about assessment. This is where many companies default to outdated methods. Long, tedious take-home assignments that ask candidates to spend days building something they’ll never actually use. These tests have low validity and unfairly filter out the busiest (and usually the best) people.

A smarter approach is a semi-structured technical interview. Sit down with the candidate and walk through scenarios together. Ask them questions that require real-time thinking, explanation, and problem-solving. The goal isn’t to get a perfect answer, it’s to see their thought process. You’re not looking for perfect answers. You’re looking for structured thinking, clear communication, and sound trade-offs. That’s what matters in production.

Skip the pre-work. Respect their time. You’ll attract stronger candidates and leave a better impression while you’re at it.

Avoiding Hiring Doom Loops

Here’s the truth no one loves to hear. AI engineers are expensive. If you go into this process expecting bargain pricing, you’re setting yourself up for disappointment. But if you think long-term, you’ll realize building internal competence is often far more cost-effective than endlessly outsourcing AI consultants.

Stay strategically flexible as you hire. Revisit your assumptions as you meet candidates. You might realize the role needs tweaking or that your expectations were off. And listen to candidate feedback. Top talent often spots misalignments faster than internal teams do.

Remember, experience does not always equal capability. Many skilled engineers come from unconventional backgrounds, side projects, or personal passion. If your filters are too rigid, you risk losing exceptional talent.

Where They Live in Your Org Map

Once you’ve found your person, you need to place them somewhere that gives them the ability to actually influence your business. In larger organizations or scaleups, AI engineers often sit within a dedicated ML/AI department or operate as a specialized support team for product-focused tech groups. In startups, the structure is usually more fluid. They might have a more broader role working closely with the Head of Tech and wear a half-dozen hats before lunch.

Hiring an AI engineer isn’t a trend-following move, it’s a strategic investment in your company’s future. Approach it with clarity. Stay realistic. Hire for potential. And give your AI talent the influence and environment they need to do what they do best.

Do that, and you’re not just keeping up. You’re building a true lasting competitive advantage.