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 stack from 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 E2E s 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 Specialization

Accelerated Computing: Understanding of NVIDIA s 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 workflows experience 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"

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Senior Solutions Architect

Job Description

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