Senior AI/ML Platform Engineer: Scale Secure ML Pipelines

Aimlroles; San Mateo (CA); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Flexible work environment

Health and wellness benefits

Paid time off programs

Market-competitive pay

Continual development

Job summary

Guidewire seeks a Senior AI/ML Platform Engineer to architect and scale the ML platform powering data scientists and ML engineers. You will work across data science, MLOps, and product engineering to enable secure, scalable AI workflows in a hybrid environment.

You will lead design discussions, mentor juniors, and drive best practices for reproducibility and governance while ensuring compliance with security and privacy requirements.

Qualifications

• Experience in building large-scale distributed systems and ML platforms.
• Proven track record in MLOps and data-driven product delivery.
• Strong programming skills in Python, Go, or Java.
• Experience with Docker, Kubernetes, and cloud services.

Responsibilities

• Architect and scale an ML platform supporting the full lifecycle.
• Design infrastructure for training, tuning, monitoring, and registry.
• Orchestrate ML workflows with Kubeflow, SageMaker, MLflow.
• Collaborate across teams to enforce reproducibility and CI/CD.
• Lead platform discussions, mentor engineers, set technical vision.
• Ensure security, privacy, and regulatory compliance.

Skills

Distributed systems

MLOps

Python

Go

Java

Docker

Kubernetes

SageMaker

Kubeflow

Cloud platforms

Communication

Leadership

Education

Bachelor's or Master's in CS/Engineering

Tools

Kubeflow

SageMaker

MLflow

Vertex AI

Databricks

Airflow

Argo

Kafka

Terraform

TeamCity

Job description

Guidewire seeks a Senior AI/ML Platform Engineer to architect and scale the ML platform powering data scientists and ML engineers. You will work across data science, MLOps, and product engineering to enable secure, scalable AI workflows in a hybrid environment.

You will lead design discussions, mentor juniors, and drive best practices for reproducibility and governance while ensuring compliance with security and privacy requirements.