ML Infrastructure: design, build, and maintain data pipelines for ML models.
Model Development: build ML models for underwriting; ensure scalability.
Collaboration: work with data scientists, engineers, and PMs to productionize models.
Performance Monitoring: track model accuracy and efficiency; drive improvements.
A/B Testing: design experiments to optimize models.
Mentorship: guide and mentor junior engineers.

🎯 Requirements

Educational: BS in CS, Engineering, Math, or related; advanced degree preferred.
Experience: 3+ years deploying ML models in production.
Programming: Python or Ruby; strong software design.
ML Frameworks: TensorFlow or PyTorch.
MLOps: CI/CD for ML models; monitoring tooling.
Cloud & Container: AWS or GCP; Docker and Kubernetes.
Analytical & Communication: strong problem solving and clear communication.

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