- Deploy and operate parallel/distributed storage systems including WEKA, VAST Data, Ceph, and DDN/Lustre
- Design storage architectures optimized for AI workload patterns such as checkpoint I/O bursts, sequential dataset reads, and KV cache for inference
- Implement multi-tenant storage isolation with per-tenant QoS, quotas, and access controls
- Configure and optimize GPU Direct Storage for direct GPU-to-storage data paths
- Deploy and manage storage networking including NFS over RDMA, NVMe-oF, high-speed storage fabrics, and Nvidia CMX for cluster-wide storage orchestration
- Diagnose and tune storage performance using IOPS, throughput, latency profiling, fio, IOR, and mdtest
- Own the runbook for common failure modes
- Plan storage capacity aligned with GPU cluster growth and customer workload projections
- Manage firmware, data migration, and disaster recovery procedures
- Instrument storage telemetry including IO tail latency, checkpoint durations, NVMe SMART, filesystem health, and RDMA counters
- Feed telemetry into the platform team's metrics, logs, and traces store
- Partner with the platform team to define the storage-fault predictor, including signals, labels, and false-positive tolerances
- Convert novel incidents into automation, progressing from SOPs to runbook-as-code and agent-executable remediation
- Deliver observability and a baseline predictor for the top three storage-fault classes
- Reduce storage-incident MTTR
- Design storage for Nvidia GB200-class clusters
Requirements
- 5+ years in enterprise or HPC storage operations, with at least 2 years supporting AI/ML workloads
- Hands-on deployment and operations experience with at least two of: WEKA, VAST Data, Ceph, DDN/Lustre
- Strong understanding of AI training I/O patterns: checkpoint frequency, dataset loading, shuffle buffers
- Experience with high-performance storage networking (NFS over RDMA, NVMe-oF)
- Knowledge of GPU Direct Storage and RDMA-based data transfer
- Proficiency in storage performance benchmarking and tuning (fio, IOR, mdtest)
- Experience implementing multi-tenant storage with isolation and QoS
- Strong Linux systems knowledge (kernel tuning, filesystem internals, block device management)
- Experience shipping an anomaly detector for storage/IO telemetry or ability to articulate the labels and features needed
- Runbook-as-code mindset, with every SOP executable by a machine within a quarter
Core Competencies
Demonstrates expertise in deploying and managing parallel and distributed storage systems optimized for AI workloads, with a strong focus on performance tuning, multi-tenant isolation, and telemetry instrumentation. Proficient in high-performance storage networking and capable of implementing automation for storage operations.
Highest-signal resume keywords
- WEKA Deployment
- Ceph Operations
- GPU Direct Storage
- Storage Performance Benchmarking
- Multi-Tenant Storage Isolation
ATS Optimization Keywords
Hard Skills
- Storage Architecture Design
- AI Workload Optimization
- Storage Performance Tuning
- Linux Systems Knowledge
- Anomaly Detection for Telemetry
Soft Skills
- Problem-Solving
- Collaboration
Industry Keywords
- Enterprise Storage Operations
- HPC Storage
- AI/ML Workloads
- Telemetry Instrumentation
- Disaster Recovery Procedures
Tools & Technologies
- NFS over RDMA
- NVMe-oF
- Fio
- IOR
- Mdtest
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Senior GPU Cloud Storage Solutions Expert – SRE SME Arbeitgeber: Jobtailor
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