AI for Software Development: What Changes for Your Team
AI for software development shrinks a team only where the work repeats. The forms it takes, the costs it adds, how to measure it, and what has to be true first.
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Verifying a talent partner means asking for evidence that they have already found and placed the exact profile you need, with the role, the stack and the month, instead of accepting a list of specialties as proof that they can. It is a short exercise, done before signing, and it matters most for the profiles that are hardest to find: AI engineering, data and security.
The usual way to choose a partner is a mix of rate comparison, case studies and how the sales call felt. That works for roles with a healthy supply, where almost any competent partner can deliver. It breaks down for scarce profiles, because every partner says the same words about them and only some can back the words up. This article explains why a list of specialties proves so little, what to ask instead, and what a good answer looks like. The point is practical: a partner's claim about a scarce profile is worth what they are willing to put in writing.
A list of specialties shows what a partner wants to sell, not what they have delivered. Adding "AI engineering" to a services page takes an afternoon. Placing an AI engineer who has shipped models to production takes reach into the markets where those engineers work, a way to assess them, and a track record of searches that closed.
As the DevelopersLATAM page on AI, security and data profiles puts it: "Anyone can list three specialties on a page. Ask for the evidence before you sign, not after."
Traditional vendor evaluation doesn't help much here. Rate cards, generic case studies and references from unrelated roles all describe the partner, not the profile. Chemistry in a sales call describes the salesperson. None of it answers the only question that matters for a scarce role: can this partner find and assess this specific person?
The three profiles are scarce for different reasons, and each one hides a different trap for a buyer evaluating a partner.
In all three, a wrong hire is expensive in a specific way: it can take months to notice, because the person looks competent until the system they own fails.
The AI, security and data profiles page lists four questions to put to any partner, with the instruction to ask for the evidence before signing. Here is what each one tests and what a useful answer contains.
"Where have they placed this exact profile, and when?" A useful answer names a role, a stack and a month: for example, a machine learning engineer working on model evaluation, placed earlier this year. An answer that says "we work with AI" or lists technologies without placements is a description of intent.
Follow up by asking how many placements of that profile they made in the last year. A partner who reaches scarce profiles knows the number. One who doesn't usually changes the subject to a profile they find more often.
"How many candidates did they see to get there?" The number tells you two things. A partner who saw very few candidates either got lucky or didn't assess carefully. A partner who can describe the funnel, how many were contacted, how many assessed, how many presented, is running a search, not forwarding résumés.
"What happens if the person doesn't work out?" and, as the page adds, who absorbs that cost. Every placement carries the risk that someone leaves early or the fit isn't there. The useful answer is specific about what the partner does and honest about its limits. The How we work page gives an example of that honesty: "we replace the person, and we're honest about what a replacement does and doesn't restore." A replacement restores the seat. It doesn't restore the context the first person built, and a partner who says otherwise hasn't been through it.
"Who assessed them technically, and how?" This is where most searches for scarce profiles succeed or fail. If the answer is a coding test, ask what the test didn't tell them. A coding test measures something real, but not whether a person can hold an unfamiliar system in their head or ask the question that reveals a requirement nobody wrote down.
Stronger answers describe engineers who do the work reviewing engineers who want to do it, often by walking through something the candidate actually built: the constraint, the choice, what they would change. Specifics come from experience; general terms come from reading. The guide on how to interview AI developers covers the same approach from the hiring team's side.
Some answers sound reassuring and tell you nothing. These are the ones worth pushing back on:
None of these on its own disqualifies a partner. Together, they describe a partner who sells the profile without reaching it.
A good answer to all four questions arrives in writing, before the contract, and it includes doubts as well as strengths. A shortlist where every candidate is excellent is usually a shortlist that hasn't been assessed. The How we work page describes the alternative: "A shortlist with our assessment attached — including what we're not sure about."
Reach is the other part of the answer. Finding a scarce profile depends on how many places a partner can look. DevelopersLATAM is part of ACL and DataArt, and the page on what that changes is specific about it: reach, and an engineer who arrives with the group's frameworks and doesn't start from a blank page.
Then compare the answers side by side. The partner who answered with names, numbers, dates and reservations is the one that reaches the profile. The one who answered with adjectives describes it.
The main downsides of using a staffing agency are cost and distance from the search. The agency's fee sits on top of the engineer's pay, and the screening happens outside your team, so a weak assessment can reach you looking like a strong match. For scarce profiles, a generalist agency may also lack the reach to find the person.
Tell a staffing agency what the role has to solve, not only which technologies it uses. Describe the system the person will own, the must-have experience, the team they will join, the timeline and the budget. A precise brief lets a good agency say quickly whether it reaches that profile, and it makes a vague answer easier to spot.
Find a good recruitment agency by starting with the profile, not the agency. Shortlist firms that have recently filled roles like yours, ask peers who hired similar engineers which firms delivered, and request evidence in writing before signing. An agency that specializes in your field and can name past placements is a safer bet than a large generalist network.
Put the four questions in writing to every partner on your shortlist, including the ones you already like. The answers will narrow the list faster than another round of calls. To see how DevelopersLATAM answers them for the profiles that are hardest to find, start with the AI, security and data profiles, or talk to the team about the position you can't fill.
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