ML Ops / Model Governance Engineer

Compunnel, Inc.; Louisville (KY); On-site; USD 120,000 - 150,000; Full time

Job summary

A technology consulting firm is seeking an ML Ops / Model Governance Engineer to oversee the lifecycle of machine learning models. You will manage model validation, deployment, and compliance, partnering with various teams to meet audit requirements. The ideal candidate should have at least 8 years of experience in MLOps and strong Python skills. A Bachelor’s or Master’s degree in a relevant field is required. This position supports scalable and reliable ML operations in a regulated environment.

Qualifications

• 8+ years of experience in MLOps or model governance roles.
• 5+ years managing ML model lifecycle governance in production.
• 4+ years of experience with model versioning and CI/CD.

Responsibilities

• Manage the complete ML model lifecycle including validation and deployment.
• Define and enforce model governance standards and policies.
• Design and maintain MLOps pipelines for model management.

Skills

MLOps

Model governance

Python

CI/CD pipelines

Model monitoring

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering or related field

Tools

MLflow

SageMaker Model Registry

AWS

Azure

GCP

Docker

Kubernetes

Job description

We are seeking an ML Ops / Model Governance Engineer to manage the end-to-end lifecycle of machine learning models, ensuring they are production-ready, compliant, observable, and governed according to enterprise and regulatory standards. This role plays a critical part in maintaining trust, transparency, and operational excellence across ML systems supporting Next Best Action (NBA) decisioning. The engineer will work closely with Applied ML Engineers, compliance, risk, and platform teams to ensure models meet strict governance and audit requirements.

Key Responsibilities

• Manage the complete ML model lifecycle including validation, approval, deployment, versioning, monitoring, retraining, and retirement
• Define, implement, and enforce enterprise model governance standards, policies, and controls
• Design and maintain MLOps pipelines for model packaging, CI/CD, environment promotion, rollback, and release management
• Develop and maintain monitoring frameworks for model performance, drift, bias, data quality, and operational health
• Implement automated retraining pipelines with controlled deployment and approval workflows
• Maintain audit trails, lineage, and documentation for models, features, datasets, and decisions
• Partner with Applied ML Engineers to ensure models meet production, explainability, and compliance standards
• Collaborate with compliance, risk, and legal teams to support regulatory reviews and audits
• Document governance processes and ensure organization-wide adherence and audit readiness
• Continuously improve ML operations for scalability, reliability, and compliance

Required Skills

• 8+ years of experience in MLOps, ML platform engineering, or model governance roles
• 5+ years of experience managing ML model lifecycle governance in production environments
• 4+ years of experience with model versioning, CI/CD pipelines, and deployment workflows
• 3+ years of experience with Python and familiarity with ML frameworks and model serving architectures
• 3+ years of experience implementing monitoring and alerting for model performance, drift, and data quality
• 5+ years of experience working with regulatory, audit, and compliance requirements for ML systems
• Strong understanding of model governance, approval workflows, and controlled releases
• Experience building and maintaining audit trails, lineage, and documentation
• Strong analytical, problem-solving, and communication skills
• Ability to collaborate effectively with technical and non-technical stakeholders

Preferred Skills

• Experience working in regulated industries such as healthcare, life sciences, financial services, or insurance
• Familiarity with model registries and governance tools such as MLflow, SageMaker Model Registry, or similar
• Knowledge of explainable AI (XAI), bias detection, and fairness frameworks
• Experience with cloud platforms such as AWS, Azure, or GCP
• Familiarity with containerization and orchestration tools like Docker and Kubernetes
• Exposure to data governance and lineage frameworks

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field, or equivalent practical experience