We are hiring a Machine Learning Engineer to work on a strategic Pre-Production project, connecting Machine Learning models to corporate infrastructure and ensuring they operate reliably, scalably, monitorably, and governably before and after entering production. You will be part of the Machine Learning Engineering team, responsible for transforming models developed by data scientists into operational products and services, ensuring technical quality, data integrity, observability, and sustainability throughout the entire model lifecycle. Hybrid – São Paulo/SP We understand that for this evolution, the following is required: • Experience in Machine Learning Engineering, Data Engineering, Data Science, DevOps, or related areas; • Experience with production pipelines, automated testing, model deployment, and monitoring; • Python; • Spark / PySpark; • SQL; • Experience with inference APIs, CI/CD, feature stores, or ML pipelines; • Experience with monitoring, logging, versioning, and governance of systems; • Ability to convert scientific prototypes and notebooks into production inference pipelines; • Experience investigating and resolving production incidents. Desirable qualifications: • MLOps (MLflow, Docker, Kubernetes, CI/CD, Grafana, Prometheus); • Feature Engineering, model evaluation, and Explainability; • Datalake/Lakehouse architecture, Apache Spark, and Unity Catalog; • Experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch; • PySpark, Dask, Metaflow, Prefect, or Airflow; • SAS Enterprise Guide.