Senior MLOps Engineer

We are seeking a Senior MLOps Engineer to join a centralized Data & Analytics Center of Excellence (CoE). This role will be responsible for deploying, monitoring, maintaining, and optimizing machine learning models in production environments, with a particular focus on traditional predictive models. The ideal candidate has recent hands-on MLOps experience, strong production support expertise, and a proven track record of ensuring model reliability, performance, and scalability.

Key Responsibilities

• Deploy, monitor, maintain, and optimize machine learning models in production environments.
• Support the full machine learning lifecycle, from model deployment through ongoing performance monitoring and maintenance.
• Monitor and troubleshoot model performance, including identifying and addressing data drift, model drift, and prediction quality issues.
• Perform root cause analysis for model failures, performance degradation, and production incidents.
• Implement corrective actions and remediation strategies to resolve production model issues.
• Develop automated monitoring, alerting, and observability frameworks for machine learning systems.
• Collaborate with Data Scientists, Data Engineers, Analytics, Operations, Marketing, and IT teams to ensure successful model deployment and adoption.
• Build and maintain CI/CD pipelines for machine learning workflows and model releases.
• Improve model governance, reliability, scalability, and operational efficiency.
• Ensure compliance with data governance, security, and regulatory requirements, including HIPAA and Medicare/Medicaid standards where applicable.
• Document production support processes, monitoring standards, and incident response procedures.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field.
• 5+ years of experience in Machine Learning Engineering, MLOps, Data Science, or a related field.
• Strong proficiency in Python and SQL.
• Experience deploying, monitoring, and supporting machine learning models in production.
• Demonstrated experience maintaining traditional predictive models, including classification, regression, forecasting, and risk-scoring models.
• Experience monitoring both:
• Data Drift
• Model Drift / Prediction Drift
• Strong understanding of model performance metrics, model monitoring, and ML observability best practices.
• Experience performing root cause analysis for model failures and production incidents.
• Proven ability to identify, troubleshoot, and resolve model-related production issues.
• Experience with feature engineering, model evaluation, and production model lifecycle management.
• Strong communication skills and ability to work cross-functionally with technical and business stakeholders.

Preferred Qualifications

• Experience with ML platforms such as Databricks, Azure ML, AWS SageMaker, Vertex AI, MLflow, Kubeflow, or similar technologies.
• Experience with containerization and orchestration tools such as Docker and Kubernetes.
• Experience with CI/CD tools and automated ML deployment pipelines.
• Experience with Spark, Hive, or other big data technologies.
• Familiarity with model monitoring tools such as Arize AI, WhyLabs, Evidently, Fiddler, DataRobot, or similar platforms.
• Experience working in regulated industries such as healthcare, insurance, or financial services.
• Strong software engineering and DevOps fundamentals.