Machine Learning Ops Engineer

H&R Block is a leader in tax preparation and financial services, seeking a Machine Learning Ops Engineer to build and maintain infrastructure for machine learning models. The role involves collaborating with data scientists to streamline workflows, automate processes, and ensure scalable ML systems in production environments.

Responsibilities:

• Work closely with data scientists and other MLOps engineers to streamline workflows, automate processes, and ensure the scalability and reliability of ML systems in production environments
• Ensure that the team is able to offer machine learning model predictions at scale
• Devlier reliable, scalable ML systems that enable teams to move models from experimentation to production quickly and safely, while maintaining high standards for performance, security, and operational excellence

Requirements:

• Bachelor's degree in a related field or the equivalent through a combination of education and related work experience
• Ability to collaborate and solve problems effectively with excellent cross-team collaboration and communication skills
• Experience with Git
• Familiarity with cloud platforms such as AWS, Azure, or GCP, including deploying and operating services in cloud environments
• 3 years minimum related work experience
• Proficiency in Python and experience applying software engineering best practices (version control, testing, and code reviews)
• Strong problem solving skills, attention to detail, and ability to troubleshoot production issues
• Working knowledge of model governance concepts, including model versioning, experiment tracking, reproducibility, and rollback strategies
• Experience with Databricks, Azure Pipelines, and major cloud platforms (AWS, Azure, and GCP)
• Experience with Docker, Kubernetes, SQL, and enterprise scale data management practices
• Working knowledge of Generative AI technologies and their operational considerations