ML Ops Engineer
3 weeks ago
Job Description:
Minimum of 6 years DevOps experience in AWS Cloud including managing ML Pipelines.
Built and Executed atleast 2 MLOps projectsin AWS cloud using Sagemaker or otherservices.
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 amust
Should able write CloudFormation scripts for dev/test/prod environments
Knowledge in Python
Should be able to build Dockerimagesindependently
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)
Forthe purposes of this engagement, extensions will beminimal 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
improvementsto specific projects deploying or being re-platformed within the infrastructure.
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