ML OPS ENGINEER

About the position

The ML Ops Engineer at the University of Washington will play a crucial role in enhancing the performance and quality of the Azure and Databricks environment. This full-time, permanent position involves developing and deploying machine learning models, managing the entire ML lifecycle, and collaborating with data scientists and IT professionals to integrate AI/ML components into core business systems. The role focuses on leveraging advanced analytics and machine learning to provide actionable insights that strengthen connections and enhance the university's impact.

Responsibilities

• Develop, automate and manage robust CI/CD pipelines for data collection, processing, and delivery.
• Leverage MLflow for tracking experiments, packaging code, and managing model versions.
• Optimize models using Python and PySpark within the Azure Databricks environment.
• Train, develop and deploy AI applications to automate processes and enhance decision-making.
• Guide system design and integrate AI/ML components into critical business systems.
• Work with vector databases and implement similarity search techniques for data retrieval.
• Create and maintain reusable code libraries to support data scientists.
• Utilize GitHub for version control and project collaboration.

Requirements

• Bachelor's degree in computer science, data science, mathematics, engineering or a related field.
• At least two years of experience in related roles, with one or more years in deploying and maintaining machine learning models in a production environment.
• Proficiency in building and managing data pipelines using Apache Spark in Azure Databricks.
• Hands-on experience in deploying machine learning models and constructing data infrastructure for scalability in Azure.
• Strong background in MLOps tools and practices, including MLflow.
• Extensive experience with machine learning techniques and programming languages like Python and Java.

Nice-to-haves

• Experience with major ML frameworks like TensorFlow, Keras, and PyTorch.
• Familiarity with cloud computing and big data technologies.

Benefits

• Generous benefits and work/life programs.
• Professional growth opportunities.
• Diversity and inclusion initiatives.