AI Storage Engineer

DAMAC is hiring an AI Storage Engineer. Level rates it ; you can apply on DAMAC.

Level's read on this job

Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
The posting mentions RAG among its tools and skills.
Level classes it as a mid-level role.
DAMAC has 27 other open roles on Level.
Posted in the last few days.

AI in this role

rag

As AI Storage Specialist, you will own the design, deployment and optimisation of high-performance storage systems that underpin DAMAC Digital's AI GPU clusters. You will work across parallel file systems, NVMe tiers, object storage and archival layers â engineering the data path from ingest to checkpoint, and ensuring our NVIDIA Blackwell and Hopper platforms operate at their designed throughput. This is a hands-on architect role: you will lead solution design, drive vendor engagements, run PoCs and benchmarks, and partner closely with networking, compute, facilities and managed-services teams across our international sites.

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 â NVMe hot tier â warm object â 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 â 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 â 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

How we rate this

AI Storage Engineer at DAMAC rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

AI Storage Engineer is an open job at DAMAC.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in Thailand.
The job was posted on November 2, 2026.
Seniority: mid.
Applications are handled by DAMAC. Apply on DAMAC

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAG

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
Describe a typical day in a role like this one: which parts run through AI directly?
If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

List these exact terms on your resume: RAG. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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