Network Solutions Architect, AI Factory Services

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Job Summary Lenovo seeks a highly experienced Network Solutions Architect to join the Hybrid Cloud Solutions and AI Offering Engineering team within SSG. This senior-level role designs, deploys, validates, and optimizes high-performance network infrastructure for Lenovo's AI Factory and GigaFactory service offerings, supporting both enterprise-scale and hyperscale GPU environments powered by NVIDIA Spectrum-X Ethernet and InfiniBand fabrics.

The architect will develop network reference architectures, deployment runbooks, performance validation procedures, and field-ready engineering documentation consumed by Lenovo Professional Services and Managed Services teams globally. This includes GPU cluster fabric design, BlueField DPU architectures, multi-tenant network isolation, high-performance RDMA and RoCEv2 deployments, and production-scale AI infrastructure supporting large distributed training and inference workloads.

The ideal candidate brings deep expertise in NVIDIA Spectrum-X, Quantum InfiniBand, and BlueField DPU technologies, along with hands-on experience architecting, deploying, validating, and troubleshooting large-scale GPU clusters. Experience supporting hyperscale AI infrastructure, high-density liquid-cooled environments, GPU cluster bring-up, infrastructure validation, performance tuning, and customer-facing technical engagements is highly desired.

Key Responsibilities AI Fabric Architecture Design

• Design GPU cluster network architectures for NVIDIA AI Factory environments utilizing:

• Spectrum-X Ethernet (Spectrum-4 SN5600 + BlueField-3 DPUs) for enterprise deployments

• Quantum XDR InfiniBand and Spectrum Ethernet for rack-scale GigaFactory deployments

• Design and validate large-scale AI fabrics supporting RDMA, RoCEv2, GPUDirect, NCCL collectives, and high-bandwidth GPU-to-GPU communications.

• Develop rail-optimized InfiniBand topologies and Spectrum-X Adaptive Routing, SHARP, congestion management, and performance optimization strategies for AI training and inference environments.

• Contribute to architecture decisions supporting large-scale distributed AI and HPC workloads.

Deployment, Validation Performance Engineering

• Lead network bring-up, validation, and production-readiness activities for GPU infrastructure.

• Develop and validate RDMA/RoCEv2 configuration guides and deployment runbooks for AI and HPC environments.

• Establish performance baselines for:

• RDMA throughput

• RoCEv2 latency

• GPU communication efficiency

• Storage and network throughput

• End-to-end infrastructure readiness

• Design failure-domain isolation strategies and resilient network architectures for large-scale AI deployments.

• Support network architecture for high-density liquid-cooled GPU environments with power-aware design considerations.

Troubleshooting Operational Engineering

• Diagnose and resolve complex InfiniBand, Ethernet, RDMA, and GPU workload performance issues.
• Analyze congestion, telemetry, traffic patterns, link utilization, routing behavior, and fabric health to identify bottlenecks and optimize performance.
• Support root cause analysis and remediation of network, storage, and infrastructure issues impacting AI workload performance.
• Develop operational runbooks and troubleshooting procedures consumed directly by Lenovo field teams and customers.

Multi-Tenant Cloud Architecture

• Design multi-tenant networking architectures supporting AI Factory, NeoCloud, and managed-service provider environments.
• Implement namespace isolation, east-west traffic segmentation, secure tenant separation, and site resiliency aligned with NVIDIA Cloud Partner Reference Architectures.
• Design BlueField-3 DPU solutions utilizing DOCA for infrastructure offload, security services, observability, and service mesh capabilities.

Customer Cross-Functional Engagement

• Collaborate with Lenovo engineering, product, ISG, and services organizations to validate designs against NVIDIA reference architectures and future hardware roadmaps.
• Work directly with customers, partners, and delivery teams to translate AI workload requirements into production-ready infrastructure solutions.
• Provide technical leadership during customer engagements, infrastructure deployments, escalations, and architecture reviews.
• Support global deployments through approximately 40-50% travel, including customer workshops, implementation support, solution validation, and executive-level technical discussions.

Basic Qualifications

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Information Technology, or related discipline.

• 5+ years of experience designing and deploying high-performance networking solutions for:

• AI infrastructure

• HPC environments

• GPU clusters

• Hyperscale cloud environments

• Experience designing and validating large-scale network fabrics supporting AI and distributed compute workloads.

• Experience with customer-facing technical consulting, architecture reviews, or deployment leadership.

Preferred Qualifications NVIDIA AI Infrastructure Expertise

• Expert-level knowledge of:

• NVIDIA Spectrum-X

• Spectrum-4 SN5600 Ethernet

• Quantum InfiniBand (HDR/NDR/XDR)

• BlueField DPUs

• DOCA SDK

• RDMA and RoCEv2

AI Fabric GPU Cluster Experience

• Experience supporting large-scale GPU cluster deployments and production AI environments.

• Deep understanding of:

• GPUDirect

• NCCL

• AI workload communication patterns

• GPU cluster validation and performance tuning

• Expertise in:

• Adaptive Routing

• SHARP

• Congestion control

• Rail-optimized InfiniBand architectures

Networking Infrastructure

• Strong experience with:

• BGP

• EVPN

• VXLAN

• MPLS

• OSPF

• IS-IS

• Leaf-Spine architectures

• Experience designing lossless Ethernet environments utilizing:

• PFC

• ECN

• DCQCN

• QoS

• Knowledge of high-performance storage networking and end-to-end infrastructure optimization.

Automation Observability

• Experience with:

• Python

• Ansible

• Terraform

• REST APIs

• Experience implementing infrastructure observability, telemetry, monitoring, and troubleshooting solutions for large-scale networks.

• Familiarity with data center operations, performance analytics, and capacity planning.

Certifications Preferred Certifications Include

• NVIDIA Certified Networking Professional
• NVIDIA InfiniBand Specialist
• Cisco CCNP Data Center
• Cisco CCIE Data Center
• OCI Networking Specialist
• AWS Solutions Architect Associate

Hybrid Schedule on campus in Morrisville, NC. 3 days in office, 2 days work from home.

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