Your work profile

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AI Data Center Architecture & Solution Design
- Design and implement AI-focused Data Center architectures aligned with Tier II, Tier III, and Tier IV standards.
- Develop end-to-end AI Data Center solutions, including retrofitting traditional CPU-based data centers into AI Factories.
- Create advisory documents, RFPs, technical proposals, and commercial proposals for AI Data Center engagements.
- Design AI infrastructure solutions across hyperscalers (AWS, Azure, GCP, OCI) and NVIDIA Cloud Partners.
- Prepare HLDs, LLDs, network diagrams, rack layouts, BOQs, and TCO models.
- AI Networking & Fabric Architecture
- Architect and deploy InfiniBand and NVIDIA Spectrum Ethernet fabrics for AI workloads.
- Design and implement Spine-Leaf network architectures using EVPN-VXLAN overlays.
- Configure and optimize BGP, ECMP, RoCE, and high-performance networking environments.
- Lead Cumulus Linux-based deployments and network automation initiatives.
- Optimize network performance, latency, throughput, and congestion management for AI environments.
- AI Compute & GPU Infrastructure
- Design and size GPU clusters using NVIDIA H100, H200, B200, B300, DGX, and AI Factory platforms.
- Perform GPU capacity planning and workload profiling for AI and ML use cases.
- Implement GPU virtualization and Multi-Instance GPU (MIG) architectures.
- Support AI training and inference infrastructure deployments.
- AI Storage & Platform Engineering
- Design AI storage solutions utilizing NAS, SAN, NVMe, Object Storage, NFS, iSCSI, Fibre Channel, and parallel file systems.
- Implement and manage Kubernetes-based AI platforms,



including OpenShift and VMware Tanzu.
- Deploy and integrate RUN and Slurm workload schedulers for GPU orchestration.
- Ensure seamless integration of AI platforms with existing enterprise infrastructure.
- Monitoring, Observability & Operations
- Implement NVIDIA UFM, NVIDIA Mission Control, and NetQ for infrastructure monitoring and observability.
- Configure telemetry, validation, troubleshooting, and fabric management workflows.
- Drive infrastructure benchmarking, performance optimization, and capacity planning initiatives.
- Support POCs, design validation exercises, production rollouts, and operational readiness activities.
- Cloud & AI Services
- Design AI infrastructure solutions across AWS, Azure, GCP, and OCI.
- Enable AI services integration across hybrid and multi-cloud environments.
- Provide guidance on AI platform adoption, scalability, and operational best practices.

Key skills required

Data Center Infrastructure

- Strong understanding of Data Center power infrastructure, including UPS, PDU, ATS, switchgear, transformers, and generators.
- Knowledge of Data Center cooling technologies such as CRAC, CRAH, liquid cooling, immersion cooling, and chiller systems.
- Experience in rack design, cabling architecture,



white space planning, and physical infrastructure design.
- Understanding of raised floors, fire suppression systems, plenum design, and facility infrastructure.

AI Networking

- Strong expertise in InfiniBand (HDR/NDR), RoCE, and Ethernet fabrics.
- Hands-on experience with NVIDIA Spectrum switches.
- Deep understanding of EVPN-VXLAN, BGP, ECMP, Spine-Leaf architecture, and network automation.
- Experience with Cumulus Linux environments.

AI Compute & Platforms

- Expertise in NVIDIA GPU platforms including DGX, H100, H200, B200, and B300.
- Experience with GPU virtualization, MIG, and AI workload optimization.
- Strong understanding of AI training and inference infrastructure.

AI Storage

- Knowledge of AI storage architectures and parallel file systems such as Lustre and GPFS.
- Experience with NAS, SAN, Fibre Channel, NVMe, NFS, iSCSI, and Object Storage technologies.

Orchestration & Container Platforms

- Experience with Kubernetes ecosystems.
- Hands-on expertise with OpenShift and VMware Tanzu.
- Experience with RUN and Slurm workload management platforms.
- Understanding of container networking for AI workloads.

AI Software Stack

- Understanding of AI infrastructure software layers including:
- LLM Models
- MLOps Platforms
- Training and Inference Frameworks
- Agentic AI
- NVIDIA AI Enterprise
- NVIDIA Licensing
- NVIDIA NVIS

Cloud Technologies

- Robust understanding of AWS, Azure, GCP, and OCI services.
- Experience designing AI and cloud-native solutions in hyperscaler environments.

📌 Director | AI / ML | Bengaluru | Engineering | Hybrid Cloud Engineering (India)
🏢 Deloitte
📍 India