Hi There, I am Mahmood Bafana – Senior Talent Acquisition Specialist with Technogenic, we are looking to hire a Talented Professional with below skill set to work with one of our clients and came across your profile and wondering if you might interested or exploring the job market, if so, Please share me your resume at Bafana.m@technogeninc.com Position: MLOPS Data Platform Engineer Location: Charlotte North Carolina 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 of 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,
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