Engineer II, Machine Learning Ops CBM Lab
Chicago, IL On-site
$72,600–$120,600 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
The Machine Learning Ops Engineer II works under general supervision and plays an active role in the design, development, and/or operationalization of machine learning models. This position involves responsibility in planning and managing artificial intelligence (AI) and machine learning (ML) lifecycle processes and contributing to the efficiency and effectiveness of deployed models. The Machine Learning Ops Engineer II will consistently demonstrate support of the Shirley Ryan AbilityLab statement of Vision, Mission and Core Values by striving for excellence, contributing to the team efforts and showing respect and compassion for patients and their families, fellow employees, and all others with whom there is contact at or in the interest of the institute. The Machine Learning Ops Engineer II will demonstrate Shirley Ryan AbilityLab Core Attributes: Communication, Accountability, Flexibility/Adaptability, Judgment/Problem Solving, Customer Service and Core Values (Hope, Compassion, Discovery, Collaboration, and Commitment to Excellence) while fulfilling job duties.
What you'll bring
- A professional level of knowledge in computer science, engineering or a related field, typically acquired through a Bachelor’s Degree.
- Minimum of 3 years of related experience working on problems of moderate scope where analysis of situations or data requires a review of a variety of factors.
- Continues to develop professional expertise, applying institute policies and procedures to resolve a variety of issues.
- Proficient in using version control systems, especially Git.
- Able to manage branches, handle merge conflicts, and understand the importance of commit history and reverting changes.
- Strong skills in Python and experience with machine learning frameworks.
- Working proficiency with Linux and Windows operating systems.
- Familiarity with tools for deploying machine learning pipelines (eg Docker, Kubenates).
- Familiarity with cloud based production pipelines offered by leading manufacturers (eg Microsoft Azure, Amazon Web Services, Google Cloud, etc).
- Ability to work independently on assigned tasks and lead small projects.
- Excellent problem-solving skills and the ability to troubleshoot complex issues.
- Good communication skills in both written and verbal forms.