Description

Implement and deploy machine learning models on AWS SageMaker while managing data infrastructure and pipelines.

This role is remote.

Responsibilities

• Build and maintain ML model deployment pipelines on AWS SageMaker using Python.
• Manage data storage, processing workflows, and warehousing with Amazon S3 and Redshift.
• Develop and manage infrastructure using Infrastructure as Code tools like Terraform, CloudFormation, or CDK.
• Configure and secure AWS resources, including defining IAM policies and roles.
• Support data engineering initiatives and optimize existing data platforms.

Required Skills

• 9+ years of professional experience in data engineering or ML operations.
• Strong proficiency in Python for data manipulation and model implementation.
• Hands-on experience with AWS SageMaker for model training and deployment.
• Expertise in AWS S3 for data storage and Redshift for data warehousing.
• Experience with AWS Glue for ETL processes and data integration.
• Familiarity with Infrastructure as Code tools (Terraform, CloudFormation, or CDK).
• Knowledge of AWS IAM for security and access management.

Preferred Skills

• Experience implementing credit risk models or similar financial ML use cases.
• Deep understanding of data engineering platforms and architectural best practices.

Key Skills

Education

Any Graduate