Platform Support Architect

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🏄 California – Remote

🏄 California – Remote

💵 $175k - $200k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📞 Support Engineer

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DDN

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1998

🤖 Artificial Intelligence

💰 $10M Funding Round on 2011-06

Artificial Intelligence • Data Center and Cloud Computing • High Performance Computing

DDN is a global leader in AI data intelligence solutions, providing high-performance computing and sophisticated data management technologies. With a focus on accelerating AI deployments and advanced data analytics, DDN's products, including the Data Intelligence Platform and advanced storage systems, serve diverse sectors such as healthcare, financial services, and government. DDN is committed to transforming enterprise data infrastructure to leverage the full potential of AI and drive operational efficiency.

📋 Description

• Act as the primary NVIDIA AI Enterprise and vector database solutions expert for HyperPOD customer environments • Own complex end‑to‑end triage across GPU, NVAIE services, vector DB, Kubernetes, Docker, high‑speed networking, and Infinia storage • Diagnose and resolve performance bottlenecks in RAG and agentic AI workflows • Collect and interpret logs and telemetry across Linux, containers, Kubernetes, GPU stack, vector DB, and storage/networking • Author and maintain support triage runbooks and checklists for HyperPOD • Define and validate unified diagnostics bundles that capture the right logs/configs/metrics from all relevant layers • Build hands‑on labs and PoCs that mirror customer RAG and agentic AI use cases on HyperPOD • Develop reusable technical assets – implementation guides, best‑practice playbooks, tuning checklists, example architectures • Provide structured feedback from early field cases and PoCs into Product Management and Engineering

🎯 Requirements

• 5+ years in Linux‑based infrastructure roles (SRE, MLOps, platform engineering, or L2/L3 support) • Strong hands-on experience with containers and Kubernetes (Docker/containerd, Helm, Operators; debugging pods, DaemonSets, CSI, CNI, and ingress/load balancers) • Demonstrated experience operating GPU‑accelerated workloads in production: NVIDIA GPUs, drivers, CUDA concepts, GPU utilization/perf triage • Familiarity with DGX / HGX or similar GPU cluster platforms • Practical experience with AI storage and networking for HPC/AI clusters: High‑performance storage systems (e.g., EXAScaler/Lustre, GPFS, Ceph, distributed object storage, enterprise NAS/SAN) • RDMA‑accelerated and/or high‑speed Ethernet/InfiniBand networking, including fabrics, switch topologies, and large‑scale deployments • Hybrid cloud or cloud‑adjacent patterns (Kubernetes CSI, cloud‑native fabrics, data locality) • Experience with one or more vector databases (Milvus, Qdrant, Pinecone, pgVector, OpenSearch/Elasticsearch vectors, etc.), including schema design, ingestion, and operations • Solid understanding of RAG and Generative AI workflows: embeddings, retrieval, reranking, prompt design, context management, and how these interplay with vector search and GPU inference at scale • Familiarity with NVIDIA AI Enterprise components and toolchain, for example: NVIDIA NIM inference microservices, NVIDIA NeMo framework / NeMo Retriever / NeMo Curator, Triton Inference Server, TensorRT / TensorRT‑LLM, CUDA libraries, NVIDIA blueprints for enterprise RAG and agentic AI • Experience designing, operating, or supporting MLOps / GenAI pipelines: CI/CD for models, deployment strategies, canarying/rollback, GPU resource management, monitoring and alerting for AI services • Strong diagnostic skills across Linux, containers, Kubernetes, GPUs, storage, and networking; able to quickly narrow fault domains and propose experiments or configuration changes • Track record of building reusable technical assets (runbooks, KBs, implementation guides, benchmarks, PoC templates) that improve support readiness and partner/customer success • Excellent communication skills, capable of clearly explaining complex AI platform topics to both engineers and executive stakeholders, internally and with partners.