Senior MLOps Engineer

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 Sage Maker, 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, Why Labs, Evidently, Fiddler, Data Robot, or similar platforms. Experience working in regulated industries such as healthcare, insurance, or financial services. Strong software engineering and Dev Ops fundamentals.