Job Summary Responsible for managing MLOps workflows, tools, and production support processes to ensure the stability, reliability, and performance of ML models and pipelines throughout their lifecycle. Responsibilities Design, implement, and manage MLOps workflows, tools, and operational processes for ML and GenAI solutions to ensure efficient production deployment Oversee daily stability, reliability, and operational health of ML models and pipelines to maintain consistent performance Manage model lifecycle operations including registration, versioning, deployment tracking, lineage, reproducibility, and governance to ensure compliance and traceability Implement monitoring systems for data drift, concept drift, model performance degradation, inference quality, and service-level issues to proactively detect anomalies Develop and maintain dashboards and alerting mechanisms to track model health, data quality, inference behavior, and operational metrics for timely insights Create and execute incident handling, recovery, rollback, and escalation procedures for ML-related issues to minimize downtime and impact Plan and implement data quality controls, dataset versioning, and lineage tracking solutions across the ML lifecycle to support data governance Support data governance initiatives by documenting controls, policies, and practices related to ML models, datasets, and production usage Communicate complex technical risks and solutions clearly to non-technical stakeholders to facilitate informed decision-making Apply analytical and pragmatic approaches to translate governance principles into actionable implementation plans Adapt proactively and work independently in a fast-paced environment to meet evolving operational demands Preferred competencies and qualifications Bachelor’s or Master’s degree in Computer Science or related field Hands-on experience with MLOps tools and cloud ML platforms such as MLflow, Databricks, Azure ML, or equivalents Proficient in SQL and dat