About Fundamental

Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict.

At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI.

Key responsibilities

• Lead and mentor a team of DevOps engineers, fostering technical growth and collaboration

• Define and drive the infrastructure roadmap aligned with company objectives

• Architect and oversee cloud infrastructure design and implementation

• Establish best practices, standards, and processes for infrastructure development and operations

• Partner with Engineering, Research, and FDE to align infrastructure capabilities with business needs

• Drive the evolution of Kubernetes clusters optimized for GPU workloads, Production SaaS hosting and and varied enterprise deployment models

• Champion GitOps practices using ArgoCD for continuous deployment

• Establish infrastructure as code standards using Terraform

• Define monitoring and observability strategy for distributed systems

• Collaborate with ML engineers to optimize infrastructure for model training and serving

• Own infrastructure reliability, performance, and security posture

• Implement and maintain cost optimization strategies (FinOps) for cloud resources

Must have

• 7+ years of experience in cloud infrastructure and DevOps, with 5+ years in a technical leadership role

• Proven track record of building and leading high-performing infrastructure teams

• Strong experience with AWS, GCP and Azure

• Deep expertise in Kubernetes, including multi-cluster management, GPU workload optimization, resource scheduling and autoscaling, and network policies and security