MLOps Engineer

Job Title: Senior Machine Learning Operations (MLOps) Engineer

Duration: 12+ Months Contract

Job Location: Cincinnati, OH [Onsite]

Position Summary:

• We are seeking 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.

Required Qualifications & Experience:

• 3-5 years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.

• 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.

Preferred Qualifications:

• Experience implementing enterprise Feature Stores.

• Experience designing Model Registry and ML Governance frameworks.

• Experience building real-time decisioning 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.

• Model Registry and governance processes.

• Automated CI/CD/CT pipelines.

• Real-time model deployment and scoring.

• Enterprise machine learning monitoring and observability.

• Reusable machine learning platform services that accelerate