Full-time MLOps engineers from Latin America who make model deployment routine instead of an event. Shortlist in about 5 days. Flat 20% of what you pay.
Explore complementary roles that are often hired together for greater impact.
Four steps from first call to onboarded team member. No hidden fees at any point.
See how it works ->This work prices with the AI/ML family. A mid-level AI / machine learning engineer in the United States sits around $12,500 per month (2026 salary table). MLOps engineers with Teilur start at $5,150 all-in.
They own everything between a trained model and a reliable prediction: deployment pipelines, model and data versioning, reproducible training runs, serving infrastructure, and the monitoring that catches drift before a customer does. Their success looks like nothing happening.
Most machine learning that fails commercially does not fail at the modelling stage. It fails because retraining is manual, nobody can reproduce what shipped, and the first sign that a model degraded is a complaint. This is the role that removes those failure modes, and it is usually the last one teams think to hire.
DevOps engineers ship code, which behaves the same way given the same input. Models degrade quietly as the world changes around them, so the pipeline has to account for data, versions and drift as well as deployments. A strong DevOps engineer will not automatically cover this.
DevOps and cloud infrastructure are among the deepest specializations in the region, with large certified communities across AWS, Azure and GCP. MLOps builds directly on that foundation.
This role coordinates between data scientists, engineers and operations. We evaluate English before you see a profile.
Starting at $5,150 all-in for mid-level. You pay this; 80% goes to the engineer and 20% is our fee. No subscription.
Tell us what you need and get a curated shortlist of pre-vetted candidates in about five days.