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Own model observability across auto claims models, building monitoring infrastructure, dashboards, alerts, health metrics, and remediation processes for traditional ML and GenAI systems. Detect drift, performance degradation, data-quality issues, and anomalous behavior; partner with data scientists, engineers, business stakeholders, and governance teams. Produce audit-ready reporting, evaluate model outcomes against business KPIs, support MLOps/LLMOps integration, and communicate model health findings to technical and non-technical audiences.