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

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

Comfort with the data layer ML depends on: SQL, key-value/low-latency stores (Redis, DynamoDB, or equivalent), and streaming pipelines (Kafka, Kinesis, Redpanda, or equivalent)