About The Role

ABOUT THE ROLE

This is a founding infrastructure engineering role at an early-stage enterprise AI platform startup, where you'll design and build cloud infrastructure entirely from scratch. You'll own architectural decisions end-to-end — from initial design through production — for a semantic platform serving highly regulated industries like insurance, banking, and healthcare. The choices you make here will shape how the company scales for years to come.

WHAT YOU'LL DO

Design and build cloud infrastructure from the ground up to power an enterprise-grade semantic AI platform.
Own the full infrastructure lifecycle, from architecture and deployment through scaling and ongoing operations.
Establish foundational best practices, standards, and tooling in a greenfield environment.
Build and maintain CI/CD pipelines and deployment automation systems.
Architect and manage Kubernetes clusters and container orchestration platforms in production.
Implement infrastructure monitoring, observability, and logging systems.
Design and manage database infrastructure including relational and NoSQL systems at scale.
Implement infrastructure security, networking, and access control.

WHAT WE'RE LOOKING FOR

5+ years of hands-on experience building and operating cloud infrastructure systems in production environments.
Proven experience designing and deploying infrastructure-as-code using tools such as Terraform, CloudFormation, or Pulumi.
Strong experience architecting and managing Kubernetes or other container orchestration platforms in production.
Deep familiarity with at least one major cloud platform (AWS, GCP, or Azure), including networking, storage, compute, and managed services.
Experience building and maintaining CI/CD pipelines and deployment automation.
Hands-on experience with observability and monitoring tooling (e.g., Prometheus, ELK, Datadog, or similar).
Comfort making high-impact architectural decisions independently in an early-stage environment.
Experience with data infrastructure or data pipeline systems is a plus.
Background with knowledge graphs, semantic systems, or graph databases is a plus.
Experience with ML infrastructure or AI/ML platform systems is a plus.