Description

You will bridge the gap between data science and production by enabling scalable, secure, and automated ML solutions in cloud environments.

Responsibilities

• Design and maintain scalable MLOps pipelines for model training, validation, deployment, and monitoring.
• Provision and manage infrastructure-as-code for ML and data engineering components using AWS CDK.
• Integrate models into production using Amazon SageMaker, including SageMaker Pipelines, Training Jobs, and Endpoints.
• Build scalable data pipelines using AWS Glue, S3, Athena, and Redshift.
• Implement CI/CD automation for model retraining and deployment using CodePipeline and CodeBuild.
• Set up monitoring for model drift and system health using CloudWatch and SageMaker Model Monitor.

Required Skills

• 5+ years of experience in data engineering and cloud-based ML operations.
• Strong expertise in AWS CDK using Python or TypeScript.
• Hands-on experience with Amazon SageMaker for building, training, and deploying models.
• Proficiency in Python and SQL.
• Experience with CI/CD pipelines and DevOps best practices for MLOps.
• Proficiency with version control systems such as Git.
• Familiarity with containerization tools including Docker and ECS/EKS.
• Understanding of cloud data architecture, data lakes, and distributed data processing.

Preferred Skills

• Experience with feature stores, ML monitoring tools, or model registries.
• Familiarity with Delta Lake, Databricks, or Spark.
• AWS certifications such as Machine Learning Specialty or Solutions Architect Associate.

Key Skills

Education

Any Graduate