Senior GPU Cloud Storage Solutions Expert – SRE SME

Jobtailor; Deutschland; Remote; EUR 90.000 - 140.000; Vollzeit

Zusammenfassung

Jobtailor seeks a seasoned Storage Operations Engineer to deploy and manage parallel storage systems (WEKA, VAST Data, Ceph, DDN/Lustre) and optimize AI workload I/O patterns in a large-scale environment.

You will implement multi-tenant QoS, configure high-speed networking (NFS over RDMA, NVMe-oF), and build automation from runbooks to code. Strong Linux, benchmarking (fio/IOR/mdtest) and disaster recovery planning are essential.

Qualifikationen

• 5+ years in enterprise or HPC storage operations, with at least 2 years supporting AI/ML workloads.
• Hands-on deployment and operations experience with WEKA, VAST Data, Ceph, or DDN/Lustre.
• Strong understanding of AI training I/O patterns and high-performance storage networking.
• Experience shipping anomaly detectors for storage telemetry and multi-tenant isolation.

Aufgaben

• Deploy and operate parallel/distributed storage systems including WEKA, VAST Data, Ceph, and DDN/Lustre.
• Design architectures optimized for AI workloads with efficient I/O patterns.
• Implement multi-tenant storage isolation with QoS, quotas, and access controls.
• Configure and optimize GPU Direct Storage and RDMA-based data transfer.
• Deploy and manage storage networking (NFS over RDMA, NVMe-oF).
• Profile and tune storage performance using fio, IOR, and mdtest.
• Plan capacity aligned with GPU cluster growth and workload projections.

Kenntnisse

Storage operations

AI/ML workloads

Linux systems

Performance tuning

Multi-tenant isolation

Storage networking

Telemetry instrumentation

Automation mindset

Tools

WEKA

VAST Data

Ceph

DDN/Lustre

Jobbeschreibung

• 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