MLOps Engineer: Deploy & Scale ML Pipelines

Apex Systems; Cincinnati (OH); On-site; USD 110,000 - 160,000; Full time

Job summary

Everforth Apex Systems is seeking an experienced MLOps Engineer to join our Cincinnati, OH team onsite for a 14+ month engagement. You will bridge data science experimentation with production ML, building scalable MLOps pipelines and CI/CD for enterprise AI initiatives.

You will partner with Data Scientists to deploy models, monitor performance, and optimize data pipelines in a large enterprise setting.

Qualifications

• Experience as an MLOps Engineer, Machine Learning Engineer, or Advanced Data Engineer with MLOps responsibilities.
• Expert-level Python programming skills.
• Strong SQL skills.
• Experience supporting and partnering directly with Data Scientists.
• Deep understanding of the machine learning lifecycle, including model development, training pipelines, deployment, CI/CD, and monitoring.
• Proven experience moving ML models from proof-of-concept into production environments.

Responsibilities

• Partner directly with Data Scientists to deploy and operationalize machine learning models.
• Design, build, and maintain MLOps pipelines and supporting infrastructure.
• Support the complete machine learning lifecycle, from development through deployment and monitoring.
• Develop and optimize data pipelines that support AI/ML initiatives.
• Implement CI/CD processes for machine learning and data engineering workloads.
• Monitor model performance, reliability, and production stability.
• Collaborate with engineering, data science, and business stakeholders to deliver scalable solutions.
• Help establish best practices, standards, and repeatable processes for MLOps.

Skills

MLOps

Python

SQL

CI/CD for ML

Model deployment

Data science collaboration

Machine learning lifecycle

Production ML

Tools

AWS

SageMaker

Job description

Everforth Apex Systems is seeking an experienced MLOps Engineer to join our Cincinnati, OH team onsite for a 14+ month engagement. You will bridge data science experimentation with production ML, building scalable MLOps pipelines and CI/CD for enterprise AI initiatives.

You will partner with Data Scientists to deploy models, monitor performance, and optimize data pipelines in a large enterprise setting.