The Role
The Senior AWS MLOps Engineer will manage and extend existing ML pipelines in AWS, emphasizing infrastructure as code. Responsibilities include developing documentation, serving as an infrastructure expert, collaborating with data scientists, and identifying optimization opportunities within the cloud infrastructure.
Summary Generated by Built In
Job Description
- 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.
Top Skills
AWS
Python
The Company
What We Do
Our Vision is to build a company of world-class people that helps our clients optimize business performance through data, technology and analytics.
Blend360 has two divisions:
Data Science Solutions: We work at the intersection of data, technology and analytics.
Talent Solutions: We live and breathe the digital and talent marketplace.
