• Minimum of 5 years DevOps experience in AWS Cloud including managing ML Pipelines.
• Built and Executed at least 2 MLOps projects in AWS cloud using Sagemaker or other services.

Skills:

• Experience building cloud infrastructure as code
• Expertise in MLOps best practices
• Foundational understanding of data science and data science best practice
• Experience AWS services (sagemaker, ECR, S3, lambda, step functions) is a must
• Should able write CloudFormation scripts for dev/test/prod environments
• Knowledge in Python
• Should be able to build Docker images independently
• AWS CodeCommit or Github (including github actions) experience is a must

Responsibilities:

• Maintain and extend existing data science pipelines in AWS, with an emphasis on infrastructure as code (cloudformation)
• For the purposes of this engagement, extensions will be minimal and limited to those required to support the four identified workstreams.
• Maintain and create documentation on infrastructure usage and design (confluence, github wikis, diagrams)
• Serve as the internal infrastructure expert, providing guidance to data scientists deploying models into the pipelines
• Research new optimization opportunities based on the needs of specific data science products
• Work independently and collaboratively with data scientists to implement optimizations and improvements to specific projects deploying or being re-platformed within the infrastructure.