Machine Learning Ops Engineer

H&R Block•UNAVAILABLE, Missouri

1d•Remote

About The Position

The Machine Learning Operations (MLOps) Engineer is responsible for building and maintaining the infrastructure that supports the development, deployment, and monitoring of machine learning models. Day to day you'll 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. You will ensure that the team is able to offer machine learning model predictions at scale. You will deliver 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

Nice To Haves

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

Responsibilities

Build and maintain the infrastructure that supports the development, deployment, and monitoring of machine learning models.
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.
Deliver 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.