Mercury - Senior Machine Learning Operations Engineer

• 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