AI Solution Architect

Job description

Job Overview We are seeking an experienced AI Solution Architect to design and lead end-to-end enterprise AI Factory and GPU infrastructure solutions spanning compute, high-performance networking, storage, Kubernetes, cloud, and AI/ML platforms. The role requires strong expertise in NVIDIA GPU technologies, AI workloads, scalable infrastructure architecture, security, observability, performance engineering, and capacity planning . Key Responsibilities Own end-to-end architecture for AI Factory and enterprise AI solutions from requirements through production readiness. Assess AI/ML workload requirements for training, fine-tuning, inference, batch processing, and high-performance computing. Design GPU compute architectures including NVIDIA HGX/DGX/OEM platforms, multi-GPU systems, NVLink/NVSwitch, and GPU resource allocation. Design high-performance AI networking using 100/200/400/800G Ethernet, EVPN/VXLAN, and leaf-spine architectures. Design AI storage and data architectures using object storage, parallel file systems like Ceph, WEKA, , or equivalent platforms. Define AI platform architecture across Kubernetes, HPC, container runtimes, model-serving platforms, and enterprise AI frameworks. Establish architecture standards for security, identity, tenant isolation, data protection, observability, disaster recovery, and operational resilience. Develop reference architectures, high-level/low-level designs, capacity models, bills of materials, technology evaluations, and implementation roadmaps. Lead technical evaluations, proof-of-concepts, vendor assessments, and architecture review boards. Collaborate with infrastructure, network, security, storage, cloud, data, application, and operations teams. Define performance, availability, scalability, security, and cost objectives and validate architecture against measurable acceptance criteria. Provide technical leadership during deployment, migration, integration, troubleshooting, and production transition. • Required Technical Skills AI / ML Architecture • NVIDIA AI Enterprise, NGC, CUDA, NCCL, DCGM, GPU Operator and AI platform ecosystem. • PyTorch, TensorFlow, JAX and operational understanding of training and inference workloads. • GPU scheduling, multi-tenancy, MIG/vGPU, GPU utilization and workload placement. • LLM, generative AI, RAG, fine-tuning, model serving and inference architecture. GPU & AI Factory Infrastructure • NVIDIA A100/H100/H200/B200 or equivalent GPU platforms; familiarity with next-generation systems. • NVLink, NVSwitch, PCIe topology and multi-GPU performance architecture. • DGX/HGX/OEM GPU server architecture and lifecycle management. • AI Factory capacity planning, rack density, power, cooling, commissioning and lifecycle strategy. High-Performance Networking • 100/200/400/800G Ethernet, InfiniBand, RoCEv2 and RDMA, Netris • NVIDIA ConnectX/SuperNIC, Spectrum/Spectrum-X, Quantum and BlueField DPU technologies. • BGP, EVPN/VXLAN, VRF, ECMP, VLAN, MTU, PFC, ECN, QoS and congestion management. • GPU east-west traffic, GPUDirect RDMA and network performance troubleshooting. AI Storage & Data Architecture • Parallel file systems, object storage, NFS, NVMe/NVMe-oF and high-throughput data pipelines. • Ceph, WEKA, VAST, Dell PowerScale, Pure FlashBlade, NetApp or equivalent technologies. • Data lake/lakehouse concepts, metadata, lineage, data movement and data lifecycle. • GPUDirect Storage and storage/network performance optimization. AI Platform & Orchestration • Kubernetes, GPU Operator, container runtimes and Kubernetes GPU scheduling. • HPC or other equivalent workload schedulers. • Model serving/inference platforms and MLOps platform architecture. • API gateways, service discovery, secrets management and platform integration. Cloud & Hybrid Architecture • AWS and/or Azure AI infrastructure and security services. • Hybrid cloud connectivity, IAM, private networking, cloud storage and workload placement. • Cloud cost optimization, capacity planning and FinOps considerations for GPU workloads. Security & Governance • Zero Trust, network segmentation, IAM/RBAC, PAM and workload identity. • GPU, DPU, container, Kubernetes, firmware and supply-chain security. • Encryption at rest/in transit, secrets management, audit logging and compliance controls. • AI-specific risks including data/model protection, tenant isolation and secure model access. Observability & Reliability • Prometheus, Grafana, OpenTelemetry, NVIDIA DCGM and infrastructure telemetry. • Monitoring across GPU, CPU, memory, network, storage, power and thermal domains. • High availability, backup/restore, disaster recovery, business continuity and failure-domain design. • Performance engineering, bottleneck analysis, SLO/SLA design and capacity forecasting. Architecture Deliverables • AI Factory reference architecture and solution blueprints • High-Level Design (HLD) and Low-Level Design (LLD) • Network, compute, GPU and storage architecture diagrams • Capacity, performance and scalability models • Technology evaluation and vendor comparison documents • Security architecture and threat-model inputs • Bill of Materials (BOM) and infrastructure sizing • Migration/deployment strategy and implementation roadmap • Operational readiness checklist, runbooks and acceptance criteria Experience & Qualifications • 10+ years of infrastructure, cloud, enterprise architecture or solution architecture experience, with significant AI/GPU infrastructure exposure. • Proven experience designing large-scale enterprise platforms and translating business requirements into technical architectures. • Hands-on understanding of physical infrastructure, GPU systems, networking, storage and Linux platforms. • Bachelor's degree in Computer Science, Engineering, Information Technology or related field preferred. Preferred Certifications • NVIDIA certifications or equivalent GPU/AI infrastructure credentials • AWS Solutions Architect / Azure Solutions Architect • TOGAF or equivalent enterprise architecture certification • CCNP/CCIE or equivalent networking certification • CISSP or equivalent security certification • Kubernetes certifications such as CKA/CKAD • Red Hat / Linux certifications