How to hire AI developers and engineers: what to look for and where to find them

Why the conventional AI hiring search takes so long

You've probably seen this pattern before: a job description asking for deep expertise across half a dozen frameworks, a specific number of years in production ML, and a background that somehow spans research and shipping. Then the search runs for months, and the person who could actually do the work never applies, because they don't look like what the listing describes.

The numbers back this up. A conventional search for a qualified AI domain specialist typically takes four to six months, well before you get to interviews with someone who actually fits. Independent benchmarking on senior technical roles more broadly puts the average time-to-fill around 120 days. And ManpowerGroup's 2026 Talent Shortage Survey of 39,000 employers across 41 countries found that 72% report difficulty filling roles, with AI skills topping the list globally for the first time.

None of this means qualified candidates don't exist. It usually means the search was built for the wrong profile from the start.

The search that almost failed

Consider a 200-person fintech company that needed someone to improve fraud detection using AI. Leadership assumed the role required a senior machine learning engineer, so that's what the job description asked for: a specific stack, a specific number of years in production ML, a research background. The search ran for three months and produced a handful of resumes that looked right on paper and fell apart in interviews. None of them understood fraud patterns in financial services well enough to know when a model's output was actually wrong.

Eventually, someone on the team asked a different question: did they need someone who could build a model from scratch, or someone who understood fraud detection well enough to know what a good model should catch? That reframe completely changed the search. Within 10 days, they found a risk analyst with a decade of fraud experience and enough AI fluency to evaluate and refine what the engineering team had already built. They needed someone who understood the problem AI was supposed to solve.

What to look for beyond the standard ML engineer profile

A resume full of the right frameworks doesn't tell you whether someone can catch a wrong answer in your specific context. A few things matter more than the standard checklist:

• Domain or functional fluency. Does this person understand the business problem the AI is meant to solve, beyond simply knowing the architecture? A fraud analyst, a clinician, or a supply chain planner brings context no framework list can replace.
• Practical judgment. Can they tell the difference between an output that's wrong in a way that could hurt a customer or a decision, and one that's just off by a technical margin nobody would notice?
• Cross-functional communication. Can they translate a technical output into a decision a non-technical stakeholder can act on? This is often the skill that separates a hire who ships value from one who ships code nobody trusts.

Our guide to AI talent acquisition covers more on what verification should look like once you've found someone who fits this profile.

Two different searches, two different talent pools

Most stalled AI searches are actually two searches wearing one job description. Technical AI capability, the ability to build, train, and maintain a model, is one pool: ML engineers and data scientists. Functional AI talent, the ability to deploy, evaluate, and apply AI within a specific domain, is a different pool entirely: domain experts who can tell whether an output is right for the context it's used in.

Running one search for both produces the unicorn listing that spooks qualified applicants before they even apply. Research from Textio, which has analyzed job description performance across a billion postings, found that every additional requirement narrows your applicant pool, since candidates, particularly those from underrepresented groups, are less likely to apply unless they meet nearly every stated qualification. A listing built for two roles at once filters out both.

The fix is straightforward: write two job descriptions, and source them through two different channels. Technical roles often do well on conventional tech job boards. Functional AI talent tends to be embedded in domain-specific professional networks, well outside the usual engineering job boards.

How to write a job description that actually attracts the right candidates

A good job description does more than list requirements; it's often the first signal a candidate gets about whether they'd actually fit. A few things make the difference:

• Lead with the problem the role solves. A candidate who understands what they'd actually be doing is far more likely to see themselves in the role than one reading a checklist of technologies.
• Separate your must-haves from your nice-to-haves, and be honest about which is which. If a specific framework isn't actually required to succeed in the role, don't list it as required. Every unnecessary line item costs you qualified candidates who assume they need to check every box before applying.
• Name the judgment or domain context that matters. If the job needs someone who can tell a false positive from a real fraud signal, say that directly. It tells the right candidates this role was built with them in mind.

Where to find them

For technical AI capability, conventional tech recruiting channels still work reasonably well. For functional AI talent, look somewhere different: credentialed professional networks built around domain expertise, where backgrounds are checked rather than self-reported.

The professionals who fill this second pool often aren't actively job hunting on traditional boards. They're working in their field already, and the AI fluency gets layered on top of expertise they've already built. Finding them usually means going to where domain professionals already are, rather than waiting for them to show up in a general tech search.

Getting this right shortens the hiring timeline and it’s what ultimately turns a stalled AI roadmap into one that actually works and deploys.

Frequently asked questions

How do I hire AI developers?

Start by separating what the role actually needs: technical capability to build and maintain a model, functional expertise to apply and evaluate it in your specific domain, or both. Write distinct job descriptions for each, and source them through the channels where each type of candidate is actually found.

What skills should an AI developer have?

Beyond the technical stack, look for domain or functional fluency, the ability to tell when an output is wrong in your specific context, and the communication skills to translate technical results into decisions non-technical stakeholders can act on.

How long does it take to hire an AI engineer?

Conventional searches for senior technical roles average around 120 days, and searches for qualified AI domain specialists specifically often stretch to four to six months. Separating the technical and functional searches, and sourcing each through the right channel, is what shortens that timeline.

See how PowerToFly places domain-qualified AI developers and engineers in as little as seven business days. Book a discovery call to talk about your specific hiring need.