Role OverviewThe AI Machine Learning Engineer bridges the gap between data science and implementation, supporting data scientists and clinicians in developing and deploying production-grade applications that leverage Machine Learning (ML) and Artificial Intelligence (AI).
What You Will Do
Works closely with data scientists, data engineers, and software engineers to develop and optimize reproducible and reusable pipelines for model training, validation, and inference. Monitors and troubleshoots AI and ML systems to ensure high availability, performance, and reliability.
Why It Might Be a Fit
The ideal candidate will have experience developing software and data infrastructure to support the validation and operationalization of predictive models, experience working with healthcare data in a HIPAA regulated environment, and familiarity with machine learning concepts and software development methodologies.
Requirements
- 2 years of experience in a Machine Learning Engineering, Data Science or Data Engineering role
- 1 year of experience in implementing machine learning algorithms in a production environment or applying software development lifecycle principles to analytics
- Experience developing software and data infrastructure to support the validation and operationalization of predictive models
- Experience with model training and inference in common machine learning libraries and frameworks
- Experience working with healthcare data in a HIPAA regulated environment
- Epic certification or badges in Cogito, Cognitive Computing Platform, Chronicles, and Interconnect
- Azure Certifications in Data Fundamentals, AI Fundamentals, Data Science, or Data Engineering
Benefits
- Opportunities for professional growth and development
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