Product Development & Innovation
How Text-to-SQL is Democratizing Data
Senior Latin American ML engineers who train, serve, and retrain your models, working your hours.
Companies hire a machine learning engineer from Deverr when the model already works and nothing is keeping it working. We place senior Latin American engineers who own training pipelines, feature engineering, inference, and the monitoring under all of it.
They join your team directly, in your repo and your cloud, and we employ them in their own country, so payroll, taxes, and compliance are handled without you opening a local entity.
An engineer who has already put models behind an endpoint knows where this breaks. They catch the feature computed one way in training and another way at serving time, the retraining job that has been failing quietly for a month, and the offline metric that never survived live traffic.
Quality control is the first question almost every team asks. Here is the filter a candidate passes before you see a profile.
Problem-solving, written and verbal communication, planning and self-management, collaboration across multifunctional teams, attention to detail, running several projects at once, and a continuous learning mindset, held to the same standard at every seniority level.
Most of it goes here rather than into the technical round, because even at mid-level we are looking for staff-level habits in how someone plans work and communicates it.
We ask what frameworks and tools a candidate uses day to day and how, because a resume listing PyTorch tells you nothing without the how.
For development roles our default is a whiteboard walkthrough rather than a timed test, so you hear how someone reasoned through a real model decision instead of reading a score.
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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Six things that hold on every machine learning placement we make.
The same seniority you would interview for locally, without the US salary band or the recruiting cycle that comes with it.
We employ your engineer in their own country and handle payroll, taxes, benefits, hardware, and compliance, so you never open a local entity.
Every engineer we place has shipped production work. You are not funding someone’s first deployment.
Candidates are assessed against your actual frameworks, cloud, and role requirements rather than a generic rubric applied to everyone.
LatAm engineers work US hours, so a failed retraining job or a latency spike gets picked up the same working day.
Scale the role up or down as the work changes, without renegotiating from scratch each time.
Four steps from first call to first deploy.
Tell us what you run, what is breaking, and what the role has to own.
You get a short list, each candidate scored and briefed against your requirements.
You run your own interviews. We coordinate scheduling and answer anything the scoring raised.
Your ML engineer starts in your repo, cloud, and Slack, with payroll already handled.
If the gap on your team sits either side of the model, these are the roles we fill next to it.
Most placements run one to two weeks from first call to first day, covering sourcing, vetting, interviews, and onboarding. Flag an urgent role on the call and we prioritize it.
Yes. Some teams want one dedicated engineer across modeling and infrastructure. Others already have data scientists and need someone purely on serving, orchestration, and monitoring.
Yes. We handle payroll, taxes, benefits, and compliance in every LatAm country we place in, so you do not need an entity of your own.
Common ground includes Python, PyTorch, TensorFlow, scikit-learn, MLflow, Airflow, Docker, and Kubernetes, on AWS or Google Cloud. We match against your real stack.
We stay involved after every placement. If something is not right we address it quickly rather than closing the file and moving on.
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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