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# MLOps Engineer / AI ML Engineer (Specialist - Data Sciences)

- **Company:** LTM Inc
- **Location:** Tampa, FL, United States
- **Experience:** Expert
- **Salary:** $90,032.0 - $134,200.0
- **Contract:** Internship / Graduate position
- **Skills:** Continuous Delivery, Continuous Integration, DevOps, Machine Learning, Machine Learning Operations, Artificial Intelligence Markup Language (AIML)
- **Published:** August 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=51765476474f16f8

## About the Role

Seeking a candidate with a 5 to 7 years of experience in MLOps within the Blue verse ML Engineering domain to drive scalable and efficient machine learning operations, Mandatory Skills : MLOPS

## Description

* Design develop and maintain robust MLOps pipelines to streamline model deployment and monitoring Collaborate with data scientists and engineers to operationalize machine learning models ensuring scalability and reliability Implement automation for continuous integration and continuous delivery CICD of ML models Optimize infrastructure and workflows for effective model training deployment and lifecycle management Ensure compliance with security governance and quality standards in ML operations Analyze system performance and troubleshoot issues related to ML model deployment and monitoring Stay updated with the latest trends and best practices in MLOps and machine learning engineering Participate in cross functional teams to integrate ML solutions into production environments

Roles and Responsibilities

 * Lead the end-to-end implementation of MLOps frameworks within the Blueverse ML Engineering family Collaborate closely with data science teams to translate experimental models into production ready solutions Develop and maintain automated workflows for model versioning testing deployment and rollback Monitor deployed models for performance degradation and initiate retraining or tuning as necessary Mentor junior engineers and share knowledge on MLOps best practices and tools Drive continuous improvement initiatives to enhance ML operational efficiency and scalability Coordinate with infrastructure and DevOps teams to provision and manage ML environments Document processes architectures and operational procedures to ensure knowledge sharing and compliance


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