Job Description

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.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

• 6+ years of software engineering experience, including 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.

Preferred :

• Experience with real‑time model inference and streaming ML pipelines, including low‑latency 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 real‑time data pipelines and streaming technologies such as Kafka, Flink, or Spark Structured Streaming.

• Experience using TeamCity and Terraform (or similar tools) for infrastructure‑as‑code 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.