Build and operate the real-time inference service that scores models for the risk decision engine, with low latency and high availability as first-class requirements
Own model deployment infrastructure: registry and versioning, CI/CD with performance, bias, and consistency checks, shadow mode, and staged rollouts
Build model observability: availability, latency, and error monitoring, plus drift detection as a retraining trigger
Partner with Risk Data Science to take models from a clean development-to-production handoff through to production operation under MLP ownership
Implement experimentation capabilities such as champion/challenger and canary routing, and explainability outputs like SHAP attributions
Feel a strong sense of product ownership and actively seek responsibility. We self-organize on small and medium projects, and we want someone excited to help shape and build a brand-new platform team
