Senior MLOps Engineer: Platform & CI/CD for Production ML

CBTS; Cincinnati (OH); On-site; USD 96,000 - 110,000; Full time

Job summary

CBTS is seeking a Senior Machine Learning Operations (MLOps) Engineer for a 1+ year contract in Cincinnati, OH. The role focuses on building scalable ML platforms, production ML systems, and end-to-end lifecycle management including feature engineering, deployment automation, monitoring, and governance.

The candidate should have cloud experience, strong CI/CD for ML, and collaboration with data scientists, data engineers, and business stakeholders.

Qualifications

• Extensive experience designing production ML platforms and end-to-end ML lifecycle
• Strong track record deploying ML models with governance and reproducibility
• Experience building CI/CD pipelines for ML workloads in cloud environments

Responsibilities

• Design, build, and manage production-grade ML solutions
• Create scalable ML platforms supporting model development, training, validation, deployment, and monitoring
• Implement versioning, experimentation tracking, governance, and reproducibility for ML models
• Develop frameworks for batch and real-time inference workloads
• Optimize ML systems for performance, reliability, and cost efficiency
• Design and maintain CI/CD/CT pipelines for ML workflows
• Automate model testing, deployment, rollback, and retraining
• Establish IaC and automated environment provisioning
• Build tooling for lifecycle management across dev, test, and prod

Skills

MLOps

CI/CD for ML

Feature store design

Data pipelines

IaC

Model governance

Tools

Terraform

Kubeflow

MLflow

Airflow

Docker

Kubernetes

Job description

CBTS is seeking a Senior Machine Learning Operations (MLOps) Engineer for a 1+ year contract in Cincinnati, OH. The role focuses on building scalable ML platforms, production ML systems, and end-to-end lifecycle management including feature engineering, deployment automation, monitoring, and governance.

The candidate should have cloud experience, strong CI/CD for ML, and collaboration with data scientists, data engineers, and business stakeholders.