AI Storage Engineer
Free account needed to apply or save. It takes a minute, and we'll bring you right back to this job.
How well do you match this job?
Key details
- Location
- Thailand
- Work style
- Onsite
- Level
- Senior
- Job type
- Full-time
- Experience
- 7–10 yrs
- Function
- Software engineering
- Focus area
- AI / ML
- Posted
- 23h ago
Skills
ansiblebashbenchmarkingbill of materialscapacity planningdata governancedata pipelinesencryptionhigh-performance computingkuberneteslinuxllmpythonretrieval-augmented generations3technical documentationterraformvendor management
About the role
Architecture & Design
- Architect end-to-end storage solutions for large-scale AI training and inference clusters (NVIDIA DGX/HGX, SuperPOD, B300, GB300)
- Design tiered data pipelines: ingest → NVMe hot tier → warm object → cold/archive
- Integrate storage with GPUDirect Storage (GDS), NFS over RDMA, and InfiniBand/RoCE fabrics
- Produce HLDs, LLDs, BoMs and technical documentation to enterprise standards
Deployment & Operations
- Deploy and manage parallel/distributed file systems — WEKA, VAST Data, DDN EXAScaler, Scale (GPFS), Lustre, BeeGFS
- Configure multi-tenant storage for sovereign cloud and managed AI services
- Implement checkpointing, snapshot, replication and DR strategies for AI workloads
Performance & Benchmarking
- Benchmark storage throughput, IOPS and latency using IO500, MLPerf Storage and fio
- Tune systems for LLM training checkpoints, RAG pipelines and inference serving
- Capacity planning, growth modelling and cost-per-TB optimisation
Vendor & Stakeholder Management
- Lead vendor evaluations, PoCs and commercial negotiations with storage OEMs
- Partner with NVIDIA, VAST, WEKA, DDN, Pure Storage and Dell technical teams
- Present designs and progress to internal leadership and enterprise customers
REQUIRED QUALIFICATIONS
- Experience: 7+ years in enterprise storage, with a minimum of 2 years in AI, HPC or GPU-cluster environments
- File Systems: Deep expertise in one or more of WEKA, VAST Data, DDN, GPFS, Lustre, BeeGFS
- Protocols: GPUDirect Storage, NFS over RDMA, SMB, S3, NVMe-oF
- Networking: Working knowledge of InfiniBand (NDR/HDR) and RoCEv2 storage fabrics
- Platforms: Hands-on with NVIDIA reference architectures (DGX, HGX, SuperPOD, BasePOD)
- Automation: Linux administration, Python/Bash scripting, Ansible or Terraform
- Soft Skills: Strong documentation, stakeholder communication and vendor management
PREFERRED QUALIFICATIONS
- Vendor certifications from VAST DATA, WEKA, DDN, or Pure Storage
- NVIDIA-Certified Associate — AI Infrastructure & Operations
- Experience with multi-tenant sovereign cloud or AI-as-a-Service platforms
- Exposure to Kubernetes CSI drivers, Slurm, and Run:ai orchestration
- Familiarity with data governance, encryption at rest, and cross-site replication
- Prior experience deploying at Tier III+ data centres in the Middle East or APAC