The Sr. MLOps Engineer will build and own the infrastructure layer that bridges Data Science model development with operational scoring pipelines — ensuring that Paragon's predictions are delivered at scale, with full auditability and drift detection.

Key Responsibilities:
Design and implement model deployment pipelines from Azure ML to production scoring endpoints, including versioning and rollback capabilities
Build and maintain model monitoring infrastructure for drift detection, prediction quality tracking, and data quality alerting
Manage Feature Store operations: pipeline scheduling, freshness SLAs, and serving layer for both batch and near-real-time inference
Integrate Paragon scoring outputs into SCOUT operational workflows via the TPL Intel API adapter pattern
Implement automated retraining triggers based on drift thresholds or scheduled cadences
Maintain environment parity across development, staging, and production for all ML workloads on Azure
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