Senior Machine Learning Operations (MLOps) Engineer

VSG Business Solutions•Cincinnati, OH

About The Position

The Senior Machine Learning Operations (MLOps) Engineer will design, build, deploy, and maintain scalable machine learning platforms and production ML systems. The role manages the machine learning lifecycle, from data preparation and model training through deployment, monitoring, and automated retraining, and works with data scientists, data engineers, software engineers, analytics teams, and business teams to deliver reliable, secure, and scalable solutions. Experience in banking, financial services, or other regulated industries is highly desirable. The engineer will establish and improve enterprise MLOps capabilities, including feature stores, model registries and governance, automated CI/CD/CT pipelines, real-time deployment and scoring, monitoring and observability frameworks, and reusable platform services. The role also contributes to MLOps standards and engineering practices, supports enterprise adoption of modern ML engineering, and mentors junior engineers.

Requirements

At least 5 years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
At least 3 years of experience building and supporting production machine learning systems.
Hands-on experience managing the end-to-end machine learning lifecycle.
Experience deploying and operating ML workloads in AWS, Azure, or Google Cloud.
Experience supporting both batch and real-time ML solutions.
Experience working with large-scale structured and unstructured datasets.
Strong programming skills in Python and SQL.
Experience with CI/CD automation, model deployment, monitoring, and production support.
Experience with one or more of AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
Experience with ML lifecycle management, model deployment and orchestration, experiment tracking and model versioning, model registries and feature stores, model monitoring and observability, automated retraining workflows, and model governance and responsible AI.
Experience with data engineering technologies including Pandas and PySpark, Apache Spark, dbt, Snowflake, Databricks, IBM DB2, or equivalent enterprise data platforms; data lakes and cloud storage platforms.
Experience with Git, GitHub, or GitLab; Jenkins, GitHub Actions, or Azure DevOps; Terraform or other Infrastructure as Code tools; Linux administration and shell scripting; and Docker and Kubernetes.

Nice To Haves

OpenShift experience.
Experience implementing enterprise feature stores and model registries.
Experience building automated ML governance frameworks.
Experience developing real-time decisioning and scoring platforms.
Experience with fraud detection, credit risk, customer analytics, recommendation systems, or predictive analytics.
Familiarity with MLflow, Kubeflow, Airflow, Argo Workflows, Feast, or SageMaker Pipelines.
Knowledge of distributed systems and software architecture patterns.
Familiarity with AI/ML regulatory requirements and model risk management.
AWS, Azure, or GCP certifications.
Banking or financial services experience.

Responsibilities

Design, develop, deploy, and maintain production-grade machine learning solutions and reusable platforms for model development, training, validation, deployment, and monitoring.
Implement model versioning, experiment tracking, model registries, and reproducibility practices; support batch processing and real-time inference workloads.
Optimize ML systems for performance, scalability, reliability, and cost efficiency.
Build and maintain automated CI/CD/CT pipelines for machine learning workflows, including model testing, validation, deployment, rollback, and retraining.
Implement Infrastructure as Code and automated environment provisioning, and develop tools to manage the ML lifecycle across development, testing, and production.
Design and support enterprise feature stores, integrating ML solutions with data warehouses, data lakes, and operational data sources.
Develop scalable data pipelines for model training and inference; improve data quality, availability, lineage, and observability.
Monitor model performance, data quality, data drift, concept drift, and feature drift; establish dashboards, alerts, and incident management processes.
Develop automated model retraining strategies and maintain reliable, accurate, and efficient production ML systems.
Build low-latency model scoring and prediction platforms and event-driven architectures using streaming technologies.
Support real-time inference pipelines and near-real-time business decision-making.
Implement MLOps governance standards and controls, and support compliance with enterprise security, privacy, risk, and regulatory requirements.
Support model auditability, explainability, lineage tracking, and documentation; collaborate with risk, compliance, and cybersecurity teams.
Work with data scientists, data engineers, analytics teams, and software engineers to translate business requirements into scalable technical solutions.
Contribute to MLOps standards, reusable frameworks, and engineering best practices; mentor junior engineers and support enterprise adoption of modern ML engineering practices.
Help establish and improve enterprise feature store implementation and adoption, model registry and governance processes, CI/CD/CT pipelines, real-time model deployment and scoring capabilities, monitoring frameworks, and reusable ML platform services.