Profile:- Experience with Batch Transform for large-scale batch inference;
- Strong knowledge of AWS SageMaker (model training, deployment, artifact handling, versioning);
- Hands-on with Docker & custom SageMaker Docker containers;
- Familiarity with MLOps best practices (tracking, model management, CI/CD for batch predictions);
- Fluent English (written & spoken). Responsabilities:
- Monitor models for drift & performance issues;
- Automate model retraining & tuning;
- Work with MLFlow and other experiment tracking tools;
- Deploy models using AWS + Airflow. We offer:
- Personal accompaniment focused on Career Management;
- Continuous and personalized Training and Certification through our own Learning Center;
- Solidity;
- Trust and Growth;
- Competitive salary package with benefits.