MLOps Engineer

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 oninfrastructure as code (cloudformation) For the purposes of this engagement, extensions will be minimal and limited to those requiredto support the four identified workstreams. Maintain and create documentation on infrastructure usage and design (confluence, githubwikis, diagrams) Serve as the internal infrastructure expert, providing guidance to data scientists deployingmodels 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 andimprovements to specific projects deploying or being re-platformed within the infrastructure.