Role overview
Federato is on a mission to defend the right to efficient, equitable insurance for all. We enable insurers to provide affordable coverage to people and organizations facing the issues of today - the climate crisis, cyber-attacks, social inflation, etc. Our vision is understood and well funded by those behind Salesforce, Veeva, Zoom, Box, etc.
Federato is the only AI-native platform that spans the full policy lifecycle and changes the way insurance work gets done. Better decisioning is built-in, not bolted on: insurers' unique portfolio goals, strategies, rules, and appetite are part of the workflow so underwriters win the right deals, faster. From the moment a submission hits an underwriter’s inbox, AI is put to work, triaging submissions with a focus on high-appetite business, delivering real-time feedback on the portfolio, and consolidating workflows into a single proven system. Federato drives better business outcomes.
What you'll work on
- Design and implement scalable machine learning pipelines, serving prompt engineering workflows to enhance scalability and efficiency in submission intake processes across multiple insurance use cases.
- Collaborate cross-functionally, serving as a technical lead for mid and senior team members, providing mentorship and guidance to elevate team performance and technical knowledge.
- Ensure production-grade deployment standards, emphasizing scalability, reliability, and compliance with insurance data handling policies, balancing rapid iteration with stability.
- Build reusable, modular infrastructure components and CI/CD pipelines for ML and LLM workloads, enabling rapid experimentation and seamless transition from research to production.
- Champion best practices in observability, testing, and monitoring of ML systems, establishing standards for model/data drift detection, logging, and automated rollback strategies.
- Partner with Data Science leaders to shape the vision and direction of MLOps at Federato and the future of our products