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# Machine Learning Engineer III

- **Company:** V-Soft Consulting Inc.
- **Location:** Cincinnati, OH, United States
- **Experience:** Experienced
- **Contract:** Contract
- **Skills:** Amazon Web Services, Microsoft Azure, Continuous Delivery, Continuous Integration, Information Engineering, Data Warehousing, DevOps, Github, Linux System Administration, Machine Learning, Azure Machine Learning, Shell Script, Software Engineering, Unstructured Data, Feature Store, Google Cloud, Cloud Platform System, Model Validation, Gitlab, Data Lakes, Machine Learning Operations, Drift Detection, Terraform, Data Pipelines, Jenkins
- **Published:** October 22, 2026
- **Apply:** https://www.dice.com/job-detail/0bc2618d-910e-48bd-ab8d-5066f6209084

## About the Role

* 5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
 * Demonstrated experience implementing end-to-end machine learning lifecycle management.
 * Experience operating ML workloads in cloud environments (AWS, Azure, or Google Cloud Platform).
 * Experience supporting both batch and real-time machine learning solutions.
 * Experience working with large-scale structured and unstructured data.
 * 3+ years of experience building and supporting production machine learning systems.

Knowledge and Skills:

 * 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.

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.

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.

DevOps & Infrastructure

 * Git/GitHub/GitLab
 * Jenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD tools
 * Terraform or infrastructure-as-code frameworks
 * Linux administration
 * Shell scripting

Production Monitoring & Reliability

Implement model monitoring frameworks covering:

 * Model performance
 * Data quality
 * Concept drift
 * Feature drift
 * System health and reliability
 * Data drift