ML/AI Engineer

ML/AI Engineer (Databricks)

Location: Remote (Brazil) | Type: Contract | Experience: 3 to 5+ years About the Role

We're hiring an ML/AI Engineer to build and ship machine learning and AI systems on top of a modern Databricks data platform. You'll work close to the data, turning messy enterprise sources into features, models, and AI-powered applications that run reliably in production rather than living in a notebook.

The client is a data and AI platform company that modernizes how energy and heavy-industry enterprises work with their data, migrating legacy systems onto Databricks and the lakehouse and then building intelligence on top of it. The work is hands-on and full-lifecycle: you'll touch pipelines, models, and the services that put predictions in front of real users.

This role suits someone who likes living at the seam between data engineering and ML. If you've shipped models that depend on real pipelines holding up, and you want to do that work on a clean Databricks stack instead of fighting infrastructure, you'll feel at home here.

What You'll Do Build, train, and validate ML models on Databricks, with real attention to evaluation, generalization, and where the model will actually be used Engineer features and data transformations in Spark and SQL from enterprise sources that are rarely tidy Stand up MLflow-based workflows for experiment tracking, model registry, and reproducible training runs Deploy models as batch jobs or real-time endpoints, then keep them honest with monitoring for drift, latency, and cost Partner with data engineers on the lakehouse layer so the data feeding your models is reliable and well-modeled Bring LLM and retrieval techniques into the mix where they fit the problem, and skip them where they don't Apply solid engineering habits throughout: version control, testing, code review, CI/CD

Qualifications 3 to 5+ years building ML or AI systems in production, with strong Python fundamentals Hands-on experience with Databricks, including Spark and Delta Lake (Azure Databricks is a plus) Comfort across the ML lifecycle: feature engineering, training, validation, deployment, and monitoring Working knowledge of MLflow or a comparable experiment tracking and model registry tool Strong SQL and a real understanding of how data pipelines feed models Exposure to LLM-powered or retrieval-based applications, or genuine curiosity to grow into them Professional working English, since collaboration happens across a distributed team

Why Join Us You'll join a small, senior team that has been steadily building out a Databricks-based platform for demanding enterprise clients, where the data is complex and the standards for what ships are high. The work is remote and async-friendly, and your models won't sit on a shelf; they go into systems people actually depend on.

If you want ownership over real ML work on a clean, modern stack, with teammates who care about doing it right, this is a strong fit.

To apply, click Apply on LinkedIn and visit eliosai.com/browse-jobs