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 works and needs someone to keep it that way.
We place senior Latin American engineers who own training pipelines, feature engineering, inference, and monitoring for all of it.
An engineer who has already put models behind an endpoint knows where this breaks. They catch training-serving skew, retraining jobs that fail quietly, and offline metrics that slip in live traffic.
Problem-solving, written and verbal communication, planning and management, collaboration across teams, attention to detail, and a continuous learning mindset, held to the same standard at every seniority level.
We check that a candidate has caught features computed one way in training and another at serving time, the failure that quietly breaks live models.
We ask how they use PyTorch day to day, because the how is what proves depth.
We look for engineers who set up drift detection and retraining, so your model stays accurate after launch instead of decaying unnoticed.
Mubeen Ibrahim
Head of Data, Superbalist.
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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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.
Candidates are assessed against your actual frameworks, cloud, and role requirements.
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.
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.
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 and you will go straight to our calendar to pick a time that suits you. Here is what happens next:
