Senior Machine Learning Operations Engineer

What you'll do

• Build and operate low-latency, highly available model inference and deployment infrastructure.
• Develop production model observability, drift detection, and experimentation capabilities.
• Partner with Risk Data Science to transition models into production and operate them under MLP ownership.

What they ask for

• 5+ years in machine learning engineering, backend software engineering, MLOps, or a closely related field.
• Production experience deploying, serving, and operating models in low-latency, high-availability contexts.
• Strong Python backend fundamentals and experience with API frameworks such as FastAPI or Flask.
• Experience with model lifecycle tooling, production observability, and data systems including SQL, low-latency stores, and streaming pipelines.

Nice to have

• Familiarity with Snowflake, dbt, Dagster, Airflow, or similar.
• Experience in regulated or audit-sensitive environments; exposure to functional languages or Haskell, React, and TypeScript.

Pay

$166,600 — $208,300 USD base salary; $157,400 — $196,800 CAD base salary

Tools

Python, FastAPI, Flask, SQL, Redis, DynamoDB, Kafka, Kinesis

AI focus

Productionize and operate ML models for real-time fraud and financial-crime risk decisioning, including inference, monitoring, drift-triggered retraining, experimentation, and explainability outputs such as SHAP attributions.