Manager, Solutions Architecture - AI Infrastructure Build Readiness

NVIDIA•Us, CA

12h

About The Position

NVIDIA is seeking a Solutions Architect Manager to lead the AI Factory Build Enablement function. This team closes critical gaps between OEM factory execution and services handover by defining readiness standards, verifying OEM capabilities, identifying risks early, and building mitigation plans before downstream services issues consume constrained resources. In this role, you will build and lead a small team of Solutions Architects focused on OEM readiness for AI Factory build-outs. You will plan, prioritize, and advise technical teams across readiness, NPI, integration, and services handover activities. You will work across NVIDIA architecture, services, engineering, program, account, field, and partner-facing teams to create repeatable readiness practices that replace today's per-engagement fixes with standardized operating models.

Requirements

BS, MS, or equivalent experience in Computer Science, Electrical or Computer Engineering, Systems Engineering, Physics, Mathematics, or a related technical field.
8+ overall years of experience in infrastructure architecture, data center deployment, AI/HPC systems, partner enablement, technical field engagement, or large-scale systems integration.
4+ years of experience leading a team.
Prior people leadership experience, including hiring, coaching, developing, and directing technical ICs.
Strong understanding of data center infrastructure, including compute, networking, storage, cabling, power, cooling, firmware/BMC, Linux, and deployment workflows.
Experience translating complex technical requirements into readiness criteria, partner checklists, acceptance gates, or operating processes.
Ability to assess partner capability, identify execution gaps, and drive mitigation plans before issues reach deployment or handover.
Strong cross-functional leadership across architecture, engineering, program, field, account, and partner-facing organizations.
Demonstrated ability to coordinate multiple concurrent technical workstreams, set goals, track progress, and drive timely execution across complex partner engagements.
Executive-ready communication skills, including the ability to summarize risk, readiness, tradeoffs, and decision asks for senior leaders.

Nice To Haves

Experience with AI Factory, accelerated computing, rack-scale AI infrastructure, large-scale GPU systems, or HPC deployments.
Experience working with OEM, ODM, system integrator, or deployment partner ecosystems.
Familiarity with NVIDIA GPU, networking, and AI infrastructure platforms.
Experience working with services, field engineering, professional services, manufacturing partners, or deployment services teams.
Background with recruiting, mentoring, and developing technical talent in field-facing, partner-facing, or infrastructure architecture organizations.

Responsibilities

Lead the AI Factory Build Enablement pillar, including OEM readiness criteria, partner capability verification, and pre-engagement gatekeeping before services handover.
Build and manage a team of Solutions Architects who assess OEM tools, experience, deployment readiness, and mitigation needs for AI Factory build-outs.
Plan, prioritize, and advise technical teams supporting readiness, NPI, integration, and end-to-end AI infrastructure build preparation.
Own the development and adoption of standardized OEM readiness checklists, capability assessments, risk scoring, and engagement health monitoring.
Establish clear criteria for OEM-to-services handover and drive formal sign-off processes that reduce downstream deployment, test, and bring-up issues.
Orchestrate cross-functional workstreams that align architecture, engineering, services, field, program, account, and partner-facing teams around readiness milestones and execution risks.
Partner with NVIDIA services, engineering, FAE, program, account, and tool-development teams to identify recurring readiness gaps and drive them into structured corrective actions.
Create leadership visibility into readiness risk, open mitigation plans, engagement health, and critical blockers affecting AI Factory build execution.