Profile: - Experience with Batch Transform for large-scale batch inference; - Strong knowledge of AWS SageMaker (model training, deployment, artifact handling, versioning); - Hands-on with Dockere custom SageMaker Docker containers; - Familiarity with MLOps best practices (tracking, model management, CI/CD for batch predictions); - Fluent English (written e spoken).
Responsabilities: - Monitor models for drift e performance issues; - Automate model retraining e tuning; - Work with MLFlow andother 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.