Senior Machine Learning Operations Engineer

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.

Qualifications:

• 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.

Nice to Have:

• Familiarity with a modern data stack (Snowflake, dbt, Dagster, Airflow, or similar).
• Experience operating in a regulated environment.
• Exposure to functional languages or willingness to work across a stack that includes Haskell, React, and TypeScript.