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...