Senior Machine Learning Operations Engineer

Job description

About this role

Mercury's use of machine learning in risk decisioning is growing fast in scope and in stakes. Models increasingly drive real-time decisions about fraud and financial crime, and the Machine Learning Platform (MLP) team exists to build a paved path from a trained model to a reliable production deployment, speeding up iteration, and ensuring granular production observability.

Responsibilities

• - 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

Qualifications and requirements

• - 5+ years in machine learning engineering, backend software engineering, MLOps, or a closely related field
• - Production ML service experience: deploying, serving, and operating models in low-latency, high-availability contexts
• - Strong backend engineering fundamentals in Python, with API frameworks like FastAPI or Flask
• - Experience with model deployment and lifecycle tooling: model registries, CI/CD for models, versioning, and staged rollout patterns (shadow, canary, champion/challenger)
• - Experience building observability and alerting for production services: latency, errors, and ideally model-specific signals like drift