Product Development & Innovation
How Text-to-SQL is Democratizing Data
Hire AI developers, ML engineers, and the review staff behind them, for features that have to hold up after launch.
We place AI and ML hires into teams that already have something live, so the bar is production work rather than potential. AI development and ML development both punish guesswork, and the damage shows up after release rather than in code review.
The first thing we screen for is evaluation, because an engineer who cannot build an eval set ships work nobody can prove is working.
They work your business hours. Deverr employs the hire in their own country and runs payroll, benefits and local compliance, while access to your data, prompts, weights and infrastructure stays yours to grant and to revoke.
We place both, plus the staff who review what comes out, and our AI staffing solutions put two or three candidates in front of you per role rather than a long list.
AI engineers and data specialists who build intelligent systems, not just models.
Nearshore staffing for the work that keeps the business running.
Whether you hire LLM engineers for the application layer or an ML engineer for the model itself, the same five checks apply.
Candidates walk us through an eval set they built and what it caught. Measuring quality by reading a few outputs does not clear the bar.
Every hire is scored on AI or ML work that served live users, including what they changed once it was in front of them.
Model choice, retrieval, evaluation and deployment sit with one engineer, instead of arriving feature by feature with nobody holding it.
We ask what they did about token spend, caching, batching and model choice, and we expect a specific answer rather than an intention.
You grant and revoke access to your data, prompts and weights on your own terms. Deverr holds a signed NDA with every hire and never holds your data.
Four steps, and the second one is where AI hires differ from the rest.
Thirty minutes on the role, the seniority you need, and what the model actually does inside your product.
We agree whether you are hiring for the application layer or the model layer, and what the hire may reach.
Two or three candidates, each scored on production work close to yours, with their approach to evaluation already on record.
Your hire starts on your repos, pipelines and dashboards, with the access you granted, inside the current sprint.
Mubeen Ibrahim, Head of Data, Superbalist. Five-star review.
Deverr has done a great job of meeting and exceeding our expectations. What stands out about Deverr is their engineering team's ability to work efficiently without compromising quality.
Deverr built our ingestion and data modeling engine for Shopify data. They built rock-solid infrastructure, were professional, and responsive. True partners in every sense.
We needed a senior full-stack developer fast. Deverr placed someone in under two weeks who was shipping code by day three. Six months later he's one of our core team members.
We couldn't be happier with the full-stack, AI, and WordPress developers Deverr found for us. They saved us a ton of time recruiting and brought in highly experienced developers who fit our culture perfectly, at a fraction of what we'd pay for U.S. developers of the same skill level. 5 stars, I can't recommend them enough.
Alvis.Care started as my garage project, built on Lovable, held together by vision and duct tape. Within a month of bringing on Deverr, they placed Allan as our lead engineer and our code was professionalized and stabilized. Allan took ownership of the team and gave me back time to sell the vision. If you built something real and need it to become a company, this is how you do it.
Deverr is officially the gold standard for scaling a team without the usual headaches. We will never go back to traditional recruiting after seeing how effortless this was.
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Production. Candidates are scored on features that served real traffic and on the eval set they used to know it was holding up. A notebook demo does not clear that bar.
That is your call. You decide what the hire can reach and can revoke it at any point. Deverr holds a signed NDA with every hire and never holds your data.
Yes. These are full-time employees on your team, so the person who built the feature is still there when it drifts, and the context does not leave with them.
Yes. Machine learning and MLOps candidates are scored on training, serving, monitoring and retraining, which is a different bench to the application-layer engineers.
With employed staff on your team, reviewing the same way each week. Consistency between graders is what makes an eval score trustworthy, and a rotating pool does not give you it.
Fill in the form below and you will go straight to our calendar to pick a time that suits you. Here is what happens next:
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