Job Description
📋 Description 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.
