The Senior ML Engineer (MLOps) owns the transition from ad-hoc ML deployments to a registered, monitored, governed ML platform — the lifecycle every data scientist and ML practitioner across the company uses. The role also curates a wrapper layer over open-source classical ML libraries (forecasting, causal, recommender, tabular) so retail algorithms ship on company-standard adapters rather than per-team reinventions. 

Key Responsibilities:

• Define and document the end-to-end MLOps lifecycle (experiment → train → register → approve → deploy → monitor → retrain) and enforce it via CI/CD gates.
• Stand up and operate the Model Registry (MLflow / Databricks Unity Catalog Models) as the single source of truth; ensure 100% of production models are registered, versioned, and tagged with model cards.
• Implement data drift, prediction drift, and performance-degradation monitoring with appropriate alerting and retraining triggers...