Senior Solutions Architect

10-19 YearsDelhi As a Senior Solutions Architect at E2E Networks, you will be the primary technical bridge between our advanced AI/Cloud capabilities and our enterprise customers. You will design hybrid architectures that combine high-performance GPU clusters (H100/H200/RTX 6000 Pro) with robust General Purpose Compute (Linux/CPU-based) instances to power the next generation of Indian and global enterprises. Core Responsibilities End-to-End Solution Architecture: Design scalable, secure, and high-availability infrastructure. This includes integrating GPU-accelerated nodes with standard CPU-based workloads, high-speed storage (NVMe/SSD), and complex networking. Expert Documentation & Presentation: Lead the creation of professional High-Level Design (HLD) and Low-Level Design (LLD) documents. You must be able to present these to C-suite executives, simplifying complex tech into business value. Infrastructure Strategy: Conduct Data Center-level technical assessments. Advise clients on Private vs. Public vs. Sovereign Cloud architectures, focusing on dataresidency and latency for the Indian market. The "AI-First" Edge: Lead Proof-of-Concepts (PoCs) for AI model training and inference. You will guide clients on optimizing their stackfrom the hardware layer up to the orchestration layer (Kubernetes/Docker). TCO & Proposal Engineering: Collaborate with Sales to build detailed commercial proposals. You must be able to justify the Total Cost of Ownership (TCO) of E2Es specialized infra vs. generic hyperscaler offerings. Technical Qualifications Data Center & General Purpose Compute Expertise in Linux Systems: Deep knowledge of Ubuntu/CentOS/Debian environments, kernel tuning, and CLI-based management. Networking & Security: Strong understanding of VPCs, Subnetting, Firewalls, Load Balancers, and RDMA/InfiniBand for high-speed data transfer. Storage Tiers: Proficiency in architecting Block, Object, and File storage solutions for different performance tiers. Virtualization & Orchestration: Hands-on experience with KVM, VMware, and heavy expertise in Kubernetes for containerized workloads. AI & GPU SpecializationAccelerated Computing: Understanding of NVIDIAs GPU architecture (Hopper/Ampere/Ada Lovelace) and how to match specific SKUs (e.g., A100 vs. L4s) to customer workloads. AI Stack Knowledge: Familiarity with the NVIDIA AI Enterprise (NVAIE) suite and common frameworks (PyTorch, TensorFlow). GPU Cloud Architecture & Provisioning: Deep understanding of provisioning GPU instances across bare-metal and virtualized environments. Multi-GPU Orchestration: Experience with distributed training and inference.GPU Resource Scheduling: Familiarity with Slurm, KubeFlow, or Ray Cluster for task scheduling, job management, and workload optimization. Inference Optimization: Hands-on understanding of TensorRT, ONNX Runtime, and CUDA/cuDNN tuning for low-latency inference pipelines. torage & Data Pipeline Integration: Ability to design AI data pipelines that feed high-throughput training workflowsexperience with CVAT/DALI, Ceph, or NFS-over-RDMA. Ecosystem Familiarity: Exposure to NVIDIA DGX, HGX, or cloud-native GPU platforms. Soft Skills & "The Face of E2E" 10-19 YearsDelhi As a Senior Solutions Architect at E2E Networks, you will be the primary technical bridge between our advanced AI/Cloud capabilities and our enterprise customers. You will design hybrid architectures that combine high-performance GPU clusters (H100/H200/RTX 6000 Pro) with robust General Purpose Compute (Linux/CPU-based) instances to power the next generation of Indian and global enterprises. Core Responsibilities End-to-End Solution Architecture: Design scalable, secure, and high-availability infrastructure. This includes integrating GPU-accelerated nodes with standard CPU-based workloads, high-speed storage (NVMe/SSD), and complex networking. Expert Documentation & Presentation: Lead the creation of professional High-Level Design (HLD) and Low-Level Design (LLD) documents. You must be able to present these to C-suite executives, simplifying complex tech into business value. Infrastructure Strategy: Conduct Data Center-level technical assessments. Advise clients on Private vs. Public vs. Sovereign Cloud architectures, focusing on dataresidency and latency for the Indian market. The "AI-First" Edge: Lead Proof-of-Concepts (PoCs) for AI model training and inference. You will guide clients on optimizing their stackfrom the hardware layer up to the orchestration layer (Kubernetes/Docker). TCO & Proposal Engineering: Collaborate with Sales to build detailed commercial proposals. You must be able to justify the Total Cost of Ownership (TCO) of E2Es specialized infra vs. generic hyperscaler offerings. Technical Qualifications Data Center & General Purpose Compute Expertise in Linux Systems: Deep knowledge of Ubuntu/CentOS/Debian environments, kernel tuning, and CLI-based management. Networking & Security: Strong understanding of VPCs, Subnetting, F