Tagcor

Machine Learning Engineer

Assess and analyze existing AWS SageMaker workloads, including machine learning models, pipelines, and dependencies.
Design and implement a scalable migration strategy to transition workloads from SageMaker to Databricks.
Refactor and optimize ML models, feature engineering, and data pipelines for performance and cost-efficiency on Databricks.
Ensure compatibility by modifying existing SageMaker scripts, Jupyter notebooks, and APIs for Databricks runtime.
Migrate data pipelines from AWS services (S3, Glue, Athena, Redshift) to Databricks Delta Lake and Unity Catalog.
Leverage Databricks capabilities (MLflow, AutoML, Feature Store) to enhance model training, deployment, and monitoring.
Work with cross-functional teams, including data scientists, engineers, and DevOps, to ensure a smooth transition.
Optimize compute costs by implementing best practices for Databricks clusters and resource allocation.
Develop automation scripts for CI/CD and MLOps using Terraform, Git, and Databricks Workflows.
Provide documentation and training to teams for managing workloads in Databricks.