Overseeing the end-to-end machine learning lifecycle, from data preparation to model deployment and operationalization, and driving the success of machine learning projects.
Requirements
- Experience in leveraging Databricks, Azure DevOps, and other cloud services
- At least five to 10 (5 – 10) years of hands-on experience deploying and maintaining machine learning models
- Experience tracking data and model drift for models in production
- Experience building standardized feature stores is preferred
Responsibilities
- Design, implement, and manage scalable and efficient machine learning infrastructure on the Azure cloud platform paired with Azure Databricks
- Lead the implementation and optimization of machine learning workflows on the Databricks platform, utilizing its unified analytics capabilities for streamlined development and experimentation
- Implement robust CI/CD pipelines for machine learning models, enabling automated testing, deployment, and monitoring of models in production using Azure DevOps or similar tools
- Leverage Databricks' capabilities for efficient model versioning and ongoing performance tracking
- Stay informed about the latest advancements in Databricks, Azure, and machine learning technologies, and evaluate their potential impact on our projects
- Encourage experimentation and innovation within the team to optimize ML Ops processes and enhance overall model performance.
Other
- Lead and mentor a team of ML engineers, fostering a collaborative and innovative work environment
- Collaborate with other teams, such as data engineering and IS operations, to drive cross-functional initiatives and ensure smooth ML Ops processes
- Previous team leadership or supervisory experience
- Previous experience working with healthcare data is a plus
- This is a 100% remote position but you must live in the EST or CST time zones
