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Senior AI Storage Infrastructure Engineer

bitdeer

Senior 🇬🇧 English
Container Storage Interface (CSI) Weka Lustre DAOS VAST GPUDirect Storage NVMe Kubernetes RDMA InfiniBand RoCE Terraform Ansible CI/CD pipelines

Job description

About the role

Bitdeer is seeking a Senior AI Storage Infrastructure Engineer to design and implement high‑performance storage solutions for its AI‑native NeoCloud platform. The role focuses on eliminating I/O bottlenecks for large‑scale model training and inference, ensuring GPUs remain saturated with data.

Key responsibilities

  • Design, deploy, and maintain Container Storage Interface (CSI) drivers for parallel file systems such as Weka, Lustre, DAOS, and VAST.
  • Architect and implement GPUDirect Storage (GDS) integrations for direct NVMe‑to‑GPU memory transfers.
  • Develop local NVMe caching strategies to accelerate loading of massive model weights and datasets.
  • Optimize IOPS, throughput, and latency across the containerized storage stack.
  • Collaborate with GPU Systems & Fabric teams to align storage with RDMA, InfiniBand, and RoCE interconnects.
  • Implement automated monitoring, alerting, and performance tuning for storage subsystems.
  • Define storage policies, quota management, and multi‑tenancy isolation within Kubernetes.
  • Mentor junior engineers and lead architectural design reviews.

Required profile

  • Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field.
  • 5+ years experience with distributed storage systems and high‑performance file systems.
  • Deep expertise in Kubernetes CSI development and storage operators.
  • Strong hands‑on Linux kernel I/O tuning and block/file I/O knowledge.
  • Experience with RDMA, InfiniBand, RoCE networking protocols.
  • Proven track record operating large‑scale storage environments in production or HPC settings.
  • Familiarity with infrastructure automation tools such as Terraform and Ansible.

Required skills

  • Container Storage Interface (CSI) development
  • Parallel file systems: Weka, Lustre, DAOS, VAST
  • GPUDirect Storage (GDS)
  • NVMe caching and high‑throughput storage
  • Kubernetes storage operators and multi‑tenancy
  • Linux kernel I/O performance tuning
  • RDMA, InfiniBand, RoCE networking
  • Terraform, Ansible, CI/CD pipelines

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Published 2 weeks ago

Expires 1 month from now

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