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Storage Systems Architect
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3 days ago
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Important information
Contract type:
Permanent
Location:
Bengaluru, Karnataka, India
Work mode:
Onsite
Published on:
24 October 2026
What they need
Location – Bangalore / Pune / Kolkata / Indore
Summary
We are looking for a hands-on Storage Systems Architect to design and build storage software for AI workloads: NVMe and NVMe-oF data paths, flash tiering for KV cache and vector data, and GPU-to-storage paths. You will work with SSD vendors, AI platform companies and customer engineering teams, lead development, and contribute to presales.
Key Responsibilities
- Architect storage data paths for AI training and inference: NVMe, NVMe-oF (RDMA/TCP) and GPUDirect Storage.
- Design and build flash tiering for inference: KV cache offload and vector indexes (DiskANN-class) on SSD.
- Benchmark and characterize storage: latency, tail latency, IOPS and bandwidth using fio, SPDK perf and AI storage workloads.
- Lead C/C++ development in the Linux storage stack or in user space (SPDK).
- Evaluate new storage technology (Gen5/Gen6 SSDs, FDP/ZNS, CXL memory) and turn it into PoCs and customer offerings.
- Contribute to presales: solution design, technical proposals and customer architecture discussions.
- Mentor engineers and lead design reviews.
Qualifications
Education
- Bachelor’s or master’s degree in computer engineering or computer science, Electronics, Electrical, (or equivalent experience).
Experience
- 10 to 16 years in storage product engineering.
- Hands-on design or coding.
Must-Have Technical Skills
- Storage product development (block, file or object), not only storage administration.
- Linux I/O stack (blk-mq, NVMe driver, io_uring) or SPDK.
- NVMe and NVMe-oF.
- C/C++ systems programming and performance analysis (perf, eBPF).
- Data protection fundamentals: replication, snapshots, RAID/erasure coding.
Good-to-Have Technical Skills
- GPUDirect Storage, RDMA (RoCE/InfiniBand).
- KV cache offload frameworks (LMCache, NVIDIA Dynamo KVBM) and vLLM.
- Vector databases and ANN indexes (Milvus, DiskANN).
- Parallel or distributed file systems (Lustre, WEKA, DAOS, Ceph).
- SSD internals: FTL, FDP, ZNS.
- CXL memory tiering; Python.
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