Senior Machine Learning Operations (MLOps) Engineer

CBTS; Cincinnati (OH); Sur place; USD 96 000 - 110 000; Plein temps

Résumé du poste

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

Responsabilités

• 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

Connaissances

MLOps

CI/CD for ML

Feature store design

Data pipelines

IaC

Model governance

Outils

Terraform

Kubeflow

MLflow

Airflow

Docker

Kubernetes

Description du poste

Senior Machine Learning Operations (MLOps) Engineer - 1+ Year Contract thru Dec 31, 2027

Onsite 4 days a week in Downtown, Cincinnati Ohio 45202

Pay Rate: $70/hr - $80/hr W-2

CBTS is searching for a Senior Machine Learning Operations (MLOps) Engineer to design, build, and optimize scalable machine learning platforms and production ML systems. This role will focus on enabling end-to-end machine learning lifecycle management, including feature engineering, model development, deployment automation, monitoring, governance, and continuous improvement.

The ideal candidate has strong experience building and operationalizing machine learning solutions in cloud environments, developing CI/CD pipelines for ML workloads, and collaborating with data scientists, data engineers, software engineers, and business stakeholders. Experience working in highly regulated industries such as financial services, banking, healthcare, insurance, or telecommunications is highly desirable.

Responsibilities:

ML Platform Engineering & Model Operations

• Design, develop, deploy, and manage production-grade machine learning solutions.
• Build scalable and reusable machine learning platforms that support model development, training, validation, deployment, and monitoring.
• Implement best practices for model versioning, experimentation tracking, governance, and reproducibility.
• Develop frameworks to support both batch and real-time inference workloads.
• Optimize machine learning systems for performance, reliability, maintainability, and cost efficiency.
• Design and maintain automated CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows.
• Automate model testing, validation, deployment, rollback, and retraining processes.
• Establish infrastructure-as-code practices and automated environment provisioning.
• Build operational tooling to support model lifecycle management across development, testing, and production environments.

Feature Store & Data Engineering Integration

• Design and support enterprise feature stores to improve feature reuse, consistency, and model governance.
• Collaborate with data engineering teams to integrate machine learning solutions with data warehouses, data lakes, and operational data sources.
• Develop scalable data pipelines supporting both training and inference workloads.
• Optimize data availability, quality, lineage, and observability.

Production Monitoring & Reliability

• System health and reliability
• Establish alerting, observability, and incident management processes.
• Develop strategies for model retraining and continuous performance improvement.
• Build and support low-latency scoring and prediction platforms.
• Develop event-driven machine learning architectures using streaming technologies.
• Partner with business and engineering teams to enable near real-time decisioning capabilities.

Governance, Security & Compliance

• Implement MLOps governance standards and controls.
• Ensure compliance with enterprise security, privacy