Senior Machine Learning Operations Engineer

About the Role

Join our Machine Learning Platform team to build the infrastructure that powers real-time risk decisioning, enabling our Data Science colleagues to deploy and monitor models with speed and reliability. You will play a critical role in shaping our production ML lifecycle, ensuring high availability and low-latency performance for our financial services.

Responsibilities

• Build and operate real-time inference services that score models for risk decisioning with high availability.
• Own the end-to-end model deployment infrastructure, including registry, versioning, CI/CD pipelines, and staged rollouts.
• Develop robust model observability systems to monitor latency, errors, and drift detection.
• Partner with Risk Data Science to facilitate seamless development-to-production handoffs.
• Implement advanced experimentation capabilities like champion/challenger routing and model explainability outputs.

Requirements

• 5+ years of experience in machine learning engineering, backend software engineering, or MLOps.
• Proven experience deploying and operating production ML models in low-latency environments.
• Strong backend engineering fundamentals in Python, including experience with FastAPI or Flask.
• Proficiency in model lifecycle tooling, including versioning, CI/CD, and deployment strategies.
• Experience with data layer technologies such as SQL, Redis, DynamoDB, and streaming pipelines like Kafka or Kinesis.

Nice-to-Haves

• Familiarity with modern data stack tools such as Snowflake, dbt, or Airflow.
• Experience working within regulated or audit-sensitive environments.
• Exposure to functional programming languages or interest in working with Haskell, React, and TypeScript.