Elevate your P&C insurance with Guidewire's industry-leading software! Streamline workflows, enhance customer experience, and drive growth. Learn more today! Backed by Battery.
About the role
Architect and guide the design of a scalable, secure ML platform supporting the full ML lifecycle, from data ingestion to model monitoring. Design and implement infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry.
What they're looking for
- Demonstrated ability to embrace AI and apply it to your current role as well as data-driven insights to drive innovation, productivity, and continuous improvement
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
- 10+ years of software engineering experience, including 5+ years working on ML platforms or infrastructure
- Expertise in building large-scale distributed systems and microservices
- Strong programming skills in Python, Go, or Java
- Experience with containerization and orchestration (e.g., Docker, Kubernetes)
More about this role
Join Guidewire’s Product Development & Operations (PDO) team, where we deliver operational excellence and transformative innovation for the world’s leading P&C insurance software. Our team is at the forefront of AI, cloud, and data platform adoption, working collaboratively in a hybrid environment to ensure secure, scalable, and efficient solutions. We thrive on curiosity, continuous improvement, and a culture that values diverse perspectives and teamwork. ¹
As a Senior AI/ML Platform Engineer, you will architect and scale the ML platform for data scientists and ML engineers that powers Guidewire’s next-generation products. This is a high-impact role for a technical leader passionate about distributed systems, MLOps, and empowering data-driven innovation. You will help shape the future of insurance technology by enabling seamless ML workflows and accelerating the adoption of AI across Guidewire’s solutions.
Architect and guide the design of a scalable, secure ML platform supporting the full ML lifecycle, from data ingestion to model monitoring.
Design and implement infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry.
Orchestrate ML...
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