Machine Learning Ops Engineer

About the role

The Machine Learning Ops Engineer builds and maintains the infrastructure for developing, deploying and monitoring ML models, collaborating with data scientists and other MLOps engineers to automate workflows, ensure scalability and reliability, and deliver production-ready, secure and high-performing ML systems.

About the company

H&R Block is a 70-year-old leader in the tax industry that provides tax preparation services, software and financial products, and is evolving through its Block Next strategy to blend human expertise with digital innovation. The company’s purpose is to help and inspire confidence in clients and communities, and it promotes a high-performing, connected culture focused on relationships, continuous learning and meaningful impact.

Requirements

• Bachelor’s degree in a related field or equivalent combination of education and work experience
• Minimum 3 years of 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
• Proficiency in Python and experience applying software engineering best practices (version control, testing, 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
• Preferred: Experience with Databricks, Azure Pipelines, and major cloud platforms (AWS, Azure, GCP)
• Preferred: Experience with Docker, Kubernetes, SQL, and enterprise-scale data management practices
• Preferred: Working knowledge of Generative AI technologies and their operational considerations