Senior Machine Learning Operations (MLOps) Engineer

Senior Machine Learning Operations (MLOps) Engineer - 1+ Year Contract thru Dec 31, 2027 Onsite 4 days a week in Downtown, Cincinnati Ohio 45202 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.

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. Optimize machine learning systems for performance, reliability, maintainability, and cost efficiency. MLOps Pipeline Development 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. Build operational tooling to support model lifecycle management across development, testing, and production environments. Feature Store & Data Engineering Integration 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 Implement model monitoring frameworks covering: Model performance Data quality Data drift Develop strategies for model retraining and continuous performance improvement. Real-Time Machine Learning Systems 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. Ensure compliance with enterprise security, privacy, risk, and regulatory requirements. Partner with risk, compliance, and cybersecurity teams to maintain operational standards. Collaborate closely with data scientists, analytics teams, product owners, and software engineers. Drive adoption of modern machine learning engineering methodologies across the organization.

5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.

3+ years of experience building and supporting production machine learning systems.

Demonstrated experience implementing end-to-end machine learning lifecycle management.

Experience operating ML workloads in cloud environments (AWS, Azure, or GCP).

Experience supporting both batch and real-time machine learning solutions.

Experience working with large-scale structured and unstructured data.

Machine Learning & MLOps ML lifecycle management Model deployment and orchestration Model monitoring and observability Model Registry implementation Responsible AI and model governance practices Cloud Platforms Experience with one or more: AWS SageMaker Azure Machine Learning Google Vertex AI Programming Proficiency in: Python (required) SQL (required) Java, Scala, or R (preferred) Data Platforms Experience with: Databricks IBM DB2 or equivalent enterprise RDBMS platforms Data lakes and modern cloud storage platforms Data Transformation Experience with: DevOps & Infrastructure Experience with: Jenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD tools Linux administration Streaming & Real-Time Technologies - Experience with: Real-time inference pipelines Monitoring & Observability - Experience with: Cloud-native monitoring solutions

Experience designing Model Registry and ML Governance frameworks. Experience building real-time decision-making or scoring platforms. Experience supporting credit risk, fraud, customer analytics, recommendation systems, or predictive analytics use cases. Experience with MLFlow, Kubeflow, Airflow, Argo Workflows, Feast, SageMaker Pipelines, or similar MLOps platforms. Knowledge of software architecture patterns and distributed systems. Familiarity with AI/ML regulatory frameworks and model risk management practices. Relevant cloud certifications (AWS, Azure, GCP) preferred.

Desired Outcomes - The engineer will help establish and mature enterprise MLOps capabilities, including: Feature Store implementation and adoption. Automated CI/CD/CT pipelines. Real-time model deployment and scoring. Enterprise machine learning monitoring and observability. Reusable machine learning platform services that accelerate model development and deployment across data science teams.