Job Description

What You’ll Do

• Lead, mentor, and grow an ML Platform engineering team supporting multiple teams, fostering a culture of ownership, quality, and technical excellence

• Own the design, architecture, and evolution of ML platforms, covering the full ML lifecycle: training pipelines, deployment, monitoring, and CI/CD for ML

• Drive platform capabilities that enable ML teams to operate efficiently at scale, focusing on reliability, performance, security, and cost optimization

• Provide strong technical direction through architecture reviews, design discussions, and hands-on technical guidance

• Partner closely with ML engineers, data scientists, product managers, and cloud teams to translate ML requirements into robust platform solutions

• Champion best practices in MLOps, cloud-native development, and Kubernetes-based infrastructure

• Balance people leadership (hiring, performance management, career development) with technical accountability

• Contribute to cross-team initiatives that embed AI, automation, and data-driven practices into Guidewire’s product ecosystem

What You’ll Bring

Required:

• 12 - 15 years of overall software engineering experience, preferably in cloud-based or SaaS product companies

• 3 - 4+ years of recent experience in an Engineering Manager or Technical Manager role

• Strong background in backend systems, distributed systems, and platform engineering

• Proven experience building or supporting ML platforms from an infrastructure / MLOps standpoint (not just model development)

• Hands-on experience with ML platform and MLOps tools such as:
  AWS SageMaker
  Kubeflow
  MLflow or similar tooling

• Strong experience with AWS cloud services and Kubernetes-based platforms

• Solid understanding of end-to-end ML lifecycle management, including model training, deployment, monitoring, and rollback strategies

• Experience leading engineering teams, driving execution, and delivering results in complex product environments

• Strong communication, problem-solving, and stakeholder management skills
  

Preferred:

• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience)

• Experience working with large-scale enterprise SaaS platforms

• Exposure to data platforms and streaming technologies

• Familiarity with GenAI capabilities, automation, and cloud-native best practices

• Prior experience in regulated or domain-heavy industries (insurance, finance, healthcare)