About Guidewire:

Guidewire Software is the platform trusted by 540+ P&C insurers across 40+ countries to engage, innovate, and grow efficiently.

We combine Digital, Core, Analytics, and AI to deliver our platform as a cloud service. With 1600+ successful implementations and the largest R&D team in the industry, we are transforming how insurers operate globally.

About the Team:

Join our Data Platform & Analytics team, where we:

- Design & build cloud-native data platforms

- Deliver mission-critical insights for insurers

- Build secure, scalable, and reliable SaaS services

- Drive innovation using AI and modern engineering practices

You will play a pivotal role in building next-generation analytics SaaS services that power Guidewire's industry-leading cloud platform.

About the Job :

Summary :

Join Guidewire as a Machine Learning Platform Engineer and play a pivotal role in architecting and scaling our next-generation ML platform.

You will drive the design and implementation of secure, cloud-native infrastructure supporting the full ML lifecycle, from data ingestion to model monitoring.

Collaborate with cross-functional teams to deliver innovative, reliable, and efficient solutions that empower our customers and accelerate Guidewire's AI and data platform adoption.

At Guidewire, you'll help deliver measurable value and efficiency for customers by advancing operational excellence and transformative innovation.

Our Product Development & Operations (PDO) priorities focus on secure, scalable platform operations, accelerating AI and cloud adoption, and ensuring every customer is successful and referenceable.

You'll contribute to a culture of curiosity, collaboration, and responsible AI, supporting Guidewire's mission to transform the global P&C insurance industry through technology and data-driven insights.

What You'll Do :

- Design and implement core ML infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry, using cloud-native and open-source technologies.

- Contribute to evolving a scalable, secure ML platform that supports the end-to-end ML lifecycle, including data preparation, training, evaluation, deployment, and monitoring.

- Orchestrate ML workflows using tools such as Kubeflow, SageMaker, MLflow, Vertex AI, or Databricks, ensuring reproducibility and robust automation.

- Partner with Data Engineers to build reliable, high-quality data pipelines and feature pipelines that provide model-ready datasets at scale.

- Implement and improve CI/CD for ML, including automated testing, validation, and safe rollout/rollback of models and data pipelines.

- Optimize ML workload performance and cost across compute, storage, and networking layers on public cloud (AWS, GCP, or Azure).

- Embed observability and governance into the platform, including logging, tracing, model performance monitoring, and drift detection.

- Collaborate with security, compliance, and data governance teams to ensure the platform adheres to Guidewire's standards for security, privacy, and auditability.

- Provide technical leadership and mentorship to other engineers, influencing architectural decisions, coding standards, and best practices for ML platform and MLOps.

- Continuously explore and apply AI/automation (including GenAI) to improve developer and data scientist productivity, platform reliability, and operational efficiency.

What You'll Bring :

- Demonstrated ability to embrace AI and use data-driven insights to drive innovation, productivity, and continuous improvement in your current role.

- Bachelors or Masters degree in Computer Science, Engineering, or a related field.

- 5+ years of software engineering experience, including 2-3+ years working on ML platforms or infrastructure.

- Expertise in building large-scale distributed systems and microservices, with solid understanding of system design and architecture.

- Strong programming skills in Python, Go, or Java, with emphasis on writing clean, testable, maintainable code.

- Hands-on experience with containerization and orchestration, for example Docker and Kubernetes.

- Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker and how they fit into an end-to-end ML platform.

- Cloud platform experience on AWS, GCP, or Azure, including core services for compute, storage, networking, and identity.

- Experience with statistical learning algorithms (e.g., GLM, XGBoost, Random Forest) and deep learning approaches (e.g., neural networks, transformers), with a practical understanding of how they are trained and deployed.

- Strong communication, leadership, and problem-solving skills, with the ability to influence across functions and work effectively in a global, distributed environment.