Join Guidewire as a MLOps 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 Guidewires AI and data platform adoption.

At Guidewire, youll 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. Youll contribute to a culture of curiosity, collaboration, and responsible AI, supporting Guidewires 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 cloudnative and opensource 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, highquality data pipelines and feature pipelines that provide modelready 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 Guidewires 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 youll bring :

- Demonstrated ability to embrace AI and use datadriven 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 3+ years working on ML platforms or infrastructure.

- Expertise in building largescale 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.

- Handson 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 endtoend 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 problemsolving skills, with the ability to influence across functions and work effectively in a global, distributed environment.

Preferred :

- Experience with realtime model inference and streaming ML pipelines, including lowlatency serving and online feature computation.

- Deep knowledge of model governance, reproducibility, and monitoring, including experiment lineage, versioning, and approval workflows.

- Understanding of model performance metrics and drift detection, and experience implementing monitoring for data drift, concept drift, and model quality.

- Exposure to feature stores (e.g., Feast, Tecton) and workflow orchestration tools (e.g., Airflow, Argo) in production environments.

- Familiarity with regulatory and compliance considerations for ML systems, including model auditability, interpretability, and data privacy laws such as CCPA/GDPR.

- Experience with realtime data pipelines and streaming technologies such as Kafka, Flink, or Spark Structured Streaming.

- Experience using TeamCity and Terraform (or similar tools) for infrastructureascode and CI/CD of platform components.

- Domain experience in insurance or related industries (such as banking or finance), or a demonstrated ability to ramp quickly in highly regulated domains.