Senior Machine Learning Platform Engineer

Guidewire Software is at the forefront of AI, cloud, and data platform adoption in the P&C insurance software industry. They are seeking a Senior Machine Learning Platform Engineer to architect and scale their ML platform, enabling seamless ML workflows and accelerating AI adoption across their solutions.

Responsibilities:

• Architect and guide the design of a scalable, secure ML platform supporting the full ML lifecycle, from data ingestion to model monitoring
• Design and implement infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry
• Orchestrate ML workflows using tools such as Kubeflow, SageMaker, MLflow, or similar
• Collaborate with Data Scientists, MLOps engineers, Data Engineers, and Product Engineering to define best practices for reproducibility, governance, and CI/CD for ML
• Partner with Data Engineers to build robust data pipelines for model-ready datasets
• Optimize ML workload performance across compute and storage layers using cloud-native and open-source solutions
• Lead technical discussions, mentor junior engineers, and help set the technical vision for the ML platform roadmap
• Ensure compliance with security, privacy, and regulatory requirements throughout the ML lifecycle

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
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• 10+ years of software engineering experience, including 5+ years working on ML platforms or infrastructure
• Expertise in building large-scale distributed systems and microservices
• Strong programming skills in Python, Go, or Java
• Experience with containerization and orchestration (e.g., Docker, Kubernetes)
• Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks
• Cloud platform experience (AWS, GCP, or Azure)
• Experience with statistical learning algorithms (GLM, XGBoost, Random Forest) and deep learning (neural networks, transformers)
• Strong communication, leadership, and problem-solving skills
• Experience with real-time model inference and streaming ML pipelines
• Deep knowledge of model governance, reproducibility, and monitoring
• Understanding of model performance metrics and drift detection
• Exposure to feature stores (Feast, Tecton) and workflow tools (Airflow, Argo)
• Familiarity with regulatory considerations (model auditability, interpretability, data privacy laws such as CCPA/GDPR)
• Experience with real-time data pipelines (Kafka, Flink, Spark Structured Streaming)
• Experience using TeamCity and Terraform for infrastructure setup and CI/CD
• Insurance industry or related experience (banking, finance)