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# Machine Learning Ops Engineer

- **Company:** HRB Digital LLC.
- **Location:** Fenton, MO, United States
- **Experience:** Experienced
- **Salary:** $91,000.0 - $145,600.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Cloud Computing, Code Review, Python (Programming Language), Machine Learning, Software Construction, SQL Databases, Google Cloud, Cloud Platform System, Generative AI, Git, Kubernetes, Data Management, Machine Learning Operations, Software Version Control, Atlassian Bamboo, Docker, Databricks
- **Published:** August 13, 2026
- **Apply:** https://www.careerjet.com/job/us0d51c053ebab1053e716ab45899c9334/eaa

## About the Role

* 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

Preferred Qualifications:

 * 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

## Description

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.
 * 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.


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