MLOPS Data Platform Engineer

Technogen, Inc.; Kentucky; On-site; USD 120,000 - 150,000; Full time

Job summary

A leading tech recruitment firm is seeking a Machine Learning Operations (MLOps) Engineer. This role involves managing the end-to-end lifecycle of machine-learning models, ensuring they are compliant and production-ready. Candidates should have a Bachelor's or Master's degree in a relevant field, with at least eight years of experience in MLOps and model governance. The successful applicant will design MLOps pipelines, implement governance standards, and collaborate with various teams to maintain model performance and compliance standards.

Qualifications

• 8+ years of experience in MLOps, ML platform engineering, or model governance roles.
• 5+ years managing ML model lifecycle governance in production environments.
• Experience implementing monitoring and alerting for model performance.

Responsibilities

• Manage the full ML model lifecycle, including development handoff, validation, approval, deployment.
• Define and enforce model governance standards and controls.
• Design and implement MLOps pipelines for model packaging and deployment.

Skills

MLOps

Model governance

Python

CI/CD

Machine learning frameworks

Model performance 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

Innovative Talent Acquisition Lead | Specializing in Recruitment Excellence

Hi There,

I am Mahmood Bafana – Senior Talent Acquisition Specialist with Technogenic. We are looking to hire a Talented Professional with the skill set below to work with one of our clients. If you are interested in exploring the job market, please share your resume at [e-mail].

Duration: Contract

Job Summary

• We are seeking an ML Ops / Model Governance Engineer to manage the end-to-end lifecycle of machine-learning models, ensuring they are governed, compliant, observable, and production-ready. This role is critical to maintaining trust, transparency, and regulatory compliance across enterprise ML systems supporting Next Best Action (NBA) decisioning.
• The engineer will own model governance frameworks, oversee versioning, approvals, and production deployments, and implement monitoring, retraining, and audit controls. You will work closely with Applied ML Engineers, compliance, risk, and platform teams to ensure ML operations meet strict enterprise and regulatory standards.

Key Responsibilities

• Manage the full ML model lifecycle, including development handoff, validation, approval, deployment, versioning, and retirement.
• Define and enforce model governance standards, policies, and controls aligned with enterprise and regulatory requirements.
• Design and implement MLOps pipelines for model packaging, CI/CD, promotion across environments, and rollback.
• Develop and maintain model monitoring frameworks to track performance, drift, bias, data quality, and operational health.
• Implement automated retraining pipelines and controlled release mechanisms for updated models.
• Establish and maintain audit trails, lineage, and documentation for models, data, features, and decisions.
• Partner with Applied ML Engineers to ensure models meet production, explainability, and compliance standards before release.
• Collaborate with compliance, risk, and legal teams to support regulatory reviews and audits.
• Document and socialize governance processes, ensuring organizational adherence and audit readiness.
• Continuously improve ML operations to enhance reliability, scalability, and compliance.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field, or equivalent experience.
• 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, and deployment pipelines.
• 3+ years of experience in 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 in regulatory, audit, and compliance requirements for ML systems.

Preferred Qualifications

• Experience in regulated industries such as healthcare, life sciences, financial services, or insurance.
• Familiarity with model registries and governance tools (e.g., MLflow, SageMaker Model Registry, or equivalent).
• Knowledge of explainable AI (XAI), bias detection, and fairness frameworks.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• Exposure to data governance and lineage frameworks.
• Enable Skills-Based Hiring No

Best Regards,

Mahmood Bafana

Seniority level: Mid-Senior level

Employment type: Contract

Job function: Information Technology

Industries: IT Services and IT Consulting