Senior Machine learning Engineer

Guidewire Software, Inc. is hiring a Senior ML Engineer, Generative AI, to lead the development and operation of end-to-end GenAI and LLM capabilities. The role owns core ML infrastructure and production solutions that support insurance workflows, including data pipelines, model development, deployment, monitoring, and collaboration across product and engineering teams.

Responsibilities:

• 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

Requirements:

• 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 familiarity with open-source foundation models and their fine-tuning and inference
• Experience with prompt engineering, RAG and related LLM architecture patterns, and vector databases for semantic search and retrieval
• Solid knowledge of evaluating and monitoring LLM performance using NLP and LLM-assisted metrics, with a focus on safety, robustness, and user experience
• Demonstrated technical leadership: driving design decisions, leading complex engineering efforts, mentoring peers, and conducting thoughtful code and architecture reviews
• MUST HAVE GUIDEWIRE EXPERIENCE
• Bachelor's or Master's Degree in Computer Science, or equivalent practical experience
• Experience building data pipelines and features using SQL, Spark, or AWS Glue
• Experience with containerization and orchestration (Docker, Kubernetes) and creating backend APIs to expose ML/LLM services
• Familiarity with CI/CD and infrastructure-as-code practices and tools such as TeamCity and Terraform
• Background in insurance, banking, or financial services, or interest in becoming deeply fluent in P&C insurance to better shape high-value AI and GenAI use cases