ML Ops Engineer

What you'll do

• Build real-time model serving with latency controls and safe fallbacks, and own training, validation, and feature pipelines.
• Manage model versioning, evaluation, rollout and rollback; monitor performance, drift, latency, and business outcomes.
• Integrate and evaluate third-party risk models and support merchant-facing risk tools.

What they ask for

• 4+ years in MLOps, ML infrastructure, or ML platform engineering.
• Production model experience; strong Python and comfort reading and writing TypeScript/Node.
• Hands-on ML pipeline and serving tooling experience, plus containers, Kubernetes, CI/CD, and at least one major cloud provider.
• Experience building reliable feature pipelines and monitoring production models, including drift and business outcomes.

Nice to have

• Experience in fintech, payments, or another regulated environment; real-time inference in a transaction or decisioning path.
• Familiarity with fraud, risk, or credit modeling and explaining model behavior to non-ML stakeholders.

Pay

$120,000 - $180,000 USD base salary

Tools

Python, TypeScript/Node, MLflow, Kubeflow, SageMaker, Vertex, BentoML, Feast

AI focus

Operate fraud, risk, and payments models in production, and use AI-native tools such as Claude Code to help build pipelines, write evaluations, and automate model operations.