Do you still need to fill that engineering role?
AI absorbed real engineering work, and some of it isn't coming back. Here's how to tell which side of that line your open role is on, before you decide.
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A search for an AI engineer has been open for five months. The instinct is to look at the process: rewrite the description, add a channel, loosen a requirement, or run a referral push.
Sometimes that works. More often, the profile itself is scarce, and the months it takes to discover that are the most expensive part of the search.
Few engineers have shipped AI systems into production and seen them fail at scale. Many more have built a demo or read a great deal about it.
That group is small, it grew little in the last eighteen months, and a better process can't make it bigger. A sharper job description won't produce an engineer who has spent two years learning what breaks when a model meets real traffic.
Naming this clearly saves time. Teams that assume they're doing something wrong can spend six months reworking a process that was never the problem.
A better process helps in two ways:
Reaching the engineers who exist. Most of them are employed and rarely browse job boards. You can reach them, but through different channels from the ones that work for roles with plenty of candidates.
Telling them apart. This is where most searches break. The vocabulary of AI engineering is easy to learn and hard to tell apart from real experience. Someone who has read carefully often interviews better than someone who has done the work and mostly remembers what went wrong.
AI engineering gets most of the attention, but two adjacent profiles are also scarce:
Security. Every model that touches your data adds exposure. Most teams add AI capabilities faster than they review them, usually because a new capability comes with a demo and a review doesn't.
Data. Many AI projects stall because the data underneath them can't be trusted, and the team only finds out during the pilot.
A roadmap that plans for one of these three profiles and leaves out the other two usually finds the gap late, when it costs more to fix.
Many partners list these three specialties. Ask for evidence before you sign:
Where have they placed this exact profile, and when? Ask for the role, the stack, and the month.
How many candidates did they review to get there? A partner that reaches scarce profiles knows its own numbers and will share them.
What happens if the person doesn’t work out, and who covers that cost? Get the answer in writing: whether they replace the person, who pays for the search, and what a replacement can’t restore. The context the first engineer built up leaves with them, so the next person rebuilds part of it.
Who assessed them technically, and how? If the answer is a coding test, ask what the test didn't show. A strong answer involves an engineer who does this work reviewing the candidate.
These four questions show which partners can actually reach scarce profiles. We answer them in writing, and any partner on your shortlist should do the same.
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