Senior Machine Learning Operations Engineer
Core
Build and operate the real-time inference service and platform for deploying, serving, and observing machine learning models used in fraud risk decisioning.
Role type
Senior Machine Learning Operations Engineer (MLOps)
Builds
Low-latency, high-availability ML inference services and the underlying production ML lifecycle platform
Domain
Fintech / Risk Decisioning / Machine Learning Infrastructure
Deliverable
production ML models
Required skills
Python, API frameworks (FastAPI/Flask), model deployment and lifecycle tooling, observability and alerting, SQL, key-value stores (Redis/DynamoDB), streaming pipelines (Kafka/Kinesis/Redpanda)
Preferred skills
Modern data stack (Snowflake/dbt/Dagster/Airflow), regulated environment experience, functional languages (Haskell)
Technologies
FastAPI, Flask, Redis, DynamoDB, Kafka, Kinesis, Redpanda, Snowflake, dbt, Dagster, Airflow, SHAP
Responsibilities
Build and operate real-time inference services with low latency and high availability; Own model deployment infrastructure including registry, versioning, CI/CD, and staged rollouts; Build model observability including drift detection; Partner with Data Science on model handoff and production operation; Implement experimentation capabilities like champion/challenger routing
Seniority
Senior, hands-on IC
