We are looking for a hands-on Machine Learning Engineer to support the migration of AI/ML models from Azure Databricks to AWS SageMaker.
The ideal candidate will be experienced in developing and deploying ML models across cloud platforms, with a specific focus on refactoring, optimizing, and automating workflows in AWS SageMaker.
Responsibilities:
- Assist in migrating existing AI/ML models and pipelines from Azure Databricks to AWS SageMaker.
- Refactor and optimize machine learning code to ensure smooth integration with AWS SageMaker.
- Implement SageMaker Pipelines for automated training, retraining, and model deployment.
- Collaborate with data engineering and data science teams to ensure proper migration of data pipelines and model artifacts.
- Work on deploying, testing, and monitoring ML models in AWS SageMaker.
- Optimize and monitor AWS resources (EC2, S3, Lambda, API Gateway, etc.) used for machine learning workloads.
- Implement CI/CD for machine learning models using AWS tools (Gitlab, Jenkins).
- Maintain model versioning, tracking, and monitoring using SageMaker and related AWS services.
- Ensure seamless integration of models with AWS Glue, Lambda, and other AWS services.
Qualifications:
- Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
- 4+ years of hands-on experience with machine learning development and deployment.
- Experience working with Azure Databricks for developing and managing machine learning models.
- Proficiency in AWS services, particularly AWS SageMaker, EC2, Lambda, and S3.
- Strong Python programming skills, with knowledge of ML frameworks like TensorFlow, PyTorch, and Scikit-learn, etc.
- Familiarity with Spark for ML model development in Databricks.
- Experience with Docker and container-based deployments on AWS.
- Understanding of MLOps practices, such as CI/CD, versioning, and automated pipelines.
- Familiarity with cloud security best practices in the context of machine learning workloads.
Preferred Qualifications:
- Experience with SageMaker Pipelines and other MLOps tools in AWS.
- Knowledge of data engineering tools like AWS Glue and Lambda for integration purposes.
