Description:

Design, build, and operate the AI storage layer for large-scale GPU infrastructure.
Architect and evolve storage platforms across edge and core deployments, supporting AI workloads.
Combine architectural ownership with hands-on execution in storage design, deployment, and performance engineering.

Requirements:

Strong experience in designing and operating distributed storage systems for high-performance compute environments.
Proven expertise in storage architectures for AI inference or training platforms.
Deep knowledge of Linux storage and I/O stack, and experience with GPU-accelerated workloads.
Familiarity with Kubernetes storage integrations and large-scale storage clusters.
Strong automation skills using Python and/or Bash, and experience in leading technical initiatives.

Attractive compensation package reflecting expertise and experience.
Friendly, diverse work environment with flexibility and a hybrid-friendly approach.
Opportunity to be part of a fast-growing scale-up with a mission to make a positive impact.

Skills