ML Engineer
Senior ML Engineer focused on designing, developing, and operating end‑to‑end Generative AI solutions at scale, leveraging Python, advanced machine learning, cloud platforms, and production ML operations to deliver secure, reliable AI capabilities for insurance products.
Summary
Job Description
What you’ll do
Lead the design, implementation, and evolution of Guidewire’s GenAI and LLM products, including data ingestion, feature pipelines, model training, fine-tuning, deployment, and monitoring on AWS (e.g., S3, EC2, RDS, SageMaker).
Architect and build robust ML/LLM pipelines that power high-impact use cases such as claim summarization, underwriting assistance, pricing and rating intelligence, and developer productivity tools across our product portfolio.
Develop and optimize LLM solutions using techniques such as prompt engineering, retrieval-augmented generation (RAG), vector databases, and fine-tuning to deliver reliable, safe, and high-performing experiences for insurance users.
Collaborate with Product Strategy, PDO, and Professional Services teams to align GenAI capabilities with the broader Product VPMOM, Agentic AI product roadmap, and customer adoption goals (including Claims Summary, Underwriting Assistant, Codelift, and other GenAI “lifts”).
Establish and apply ML Ops best practices for CI/CD, experimentation, evaluation, observability, and responsible AI, ensuring models are auditable, secure, and production-ready at scale.
Mentor and coach engineers and data scientists, conduct code and design reviews, and champion technical excellence, including performance, reliability, and cost efficiency of AI workloads.
Partner with cross-functional teams (Security, Finance, BizTech, GTM) to ensure AI solutions adhere to data governance and security controls, and contribute to Guidewire’s mission to transform how P&C insurers do business through cloud, analytics, and AI.
At Guidewire, we foster a culture of curiosity, innovation, and responsible use of AI—empowering our teams to continuously leverage emerging technologies and data-driven insights to enhance productivity and outcomes.
What you’ll bring
Required
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.
5+ years of professional experience in Machine Learning and/or Data Science, including end-to-end delivery of production ML systems.
Deep expertise in Python and experience building scalable ML/LLM services and pipelines, ideally on AWS using services such as S3, EC2, RDS, and SageMaker.
Strong understanding of ML Ops practices for model development, deployment, monitoring, and lifecycle management (including CI/CD for ML, experiment tracking, model registries, and drift detection).
Hands-on experience with classical and gradient-boosting models (such as GLM, Random Forest, and XGBoost) and their application to real-world business problems.
Deep understanding of neural networks and transformer-based architectures for LLMs and chat models, including famil
Posted June 21, 2026