Senior Machine Learning Operations Engineer

Responsibilities

• Build and operate real-time inference services for the risk decision engine, prioritizing low latency and high availability
• Own model deployment infrastructure, including registries, versioning, CI/CD, shadow mode, and staged rollouts
• Develop model observability tools for monitoring availability, latency, error rates, and drift detection
• Partner with Risk Data Science to manage the handoff and operationalization of models from development to production
• Implement experimentation capabilities such as champion/challenger routing and canary deployments
• Drive product ownership within a self-organized team to shape the Machine Learning Platform

Requirements

• 5+ years of experience in ML engineering, backend software engineering, MLOps, or a related field
• Proven experience deploying and operating production ML services in low-latency, high-availability environments
• Strong Python backend engineering skills and experience with API frameworks like FastAPI or Flask
• Expertise in model deployment and lifecycle tooling, including CI/CD and staged rollout patterns
• Experience building observability and alerting systems for production services
• Proficiency with SQL, low-latency stores (Redis, DynamoDB), and streaming pipelines (Kafka, Kinesis, or Redpanda)

Preferred Qualifications

• Familiarity with modern data stacks such as Snowflake, dbt, Dagster, or Airflow
• Experience operating within regulated, audit-sensitive, or compliance-heavy environments
• Exposure to functional languages or a willingness to work across a stack including Haskell, React, and TypeScript

About the Company

Mercury is a fintech company dedicated to crafting an exceptional banking experience for startups. We build the tools that help businesses manage their finances safely and efficiently, with a strong focus on security, reliability, and innovation in risk decisioning.