Senior Solutions Architect, First Time Deployment HW/MEP - NVIS
NVIDIA is hiring a Senior Solutions Architect, First Time Deployment HW/MEP - NVIS in Santa Clara, United States. It pays $148k-$236k a year and Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Level rates this solutions architect role Works on AI ●●●○: the daily work is on AI products, without building the model.
- Tools and skills the posting cites include Nvidia Reference Architecture, Telemetry Systems and hardware deployment.
- Level classifies it as a senior role.
- Level lists 2,700+ other open roles at NVIDIA.
AI in this role
Oversees end-to-end planning and delivery of AI hardware infrastructure and data center deployments based on NVIDIA reference architectures.
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
Join NVIDIA as a Solution Architect on the First Time Deployment Team and contribute to shaping the process that builds the world's leading AI infrastructure from blueprint to large-scale deployment. Oversee end-to-end planning and delivery of first-time hardware deployments, partnering with product and engineering teams to turn NVIDIA reference designs into standardized, globally repeatable deployment blueprints. You will become part of the group to build, assemble, and deploy AI factories worldwide following NVIDIA's reference concepts. This involves architectural systems, power distribution, cooling systems, integration of telemetry and control systems, and all other physical infrastructure. Working closely with product and engineering teams, customers, and the partner/provider ecosystem will be essential to securing successful deployments.
What you'll be doing:
NVIS Data Center deployment planning: Collaborate with product and engineering groups to understand NVIDIA’s reference architectures for data center infrastructure, encompassing power distribution, cooling systems, controls and monitoring, and network/cabling layout. Assist customers and partners in quickly integrating this architecture into sophisticated and dependable data center projects.
Build and construction oversight: Review and appraise customers' and partners' infrastructure build plans, verifying their compliance with NVIDIA reference architecture, industry standards, and regulatory requirements. Deliver mentorship, expertise, and suggestions to optimize performance, scalability, and cost-effectiveness.
Ensure collaboration with our customers and partners on reference architecture, guidelines, and processes to support their successful deployments.
Analyze the operational efficiency, reliability, and readiness of data center infrastructure components before launching AI/HPC clusters. Prepare and complete comprehensive audit plans and conduct pre-deployment audits to detect potential issues, risks, and areas for improvement.
Work in close partnership with engineering, product, infrastructure, and customer-facing groups to guarantee consistency on reference architecture, deployment standards, and readiness criteria.
Quality Assurance: Implement and make quality assurance processes to ensure that deployments meet established specifications and performance benchmarks. Conduct detailed bring-up, testing, and commissioning to validate the functionality and reliability of infrastructure components.
Continuous Improvement: Drive continuous improvement initiatives to improve reliability, resilience, and sustainability. Find opportunities to streamline processes, automate repetitive tasks, and apply new technologies to optimize infrastructure operations.
Collaboration and Communication: Cooperate and exchange information across internal teams, external vendors, and customers to guarantee the smooth integration of data center infrastructure solutions. Serve as a subject matter expert and main contact for infrastructure-related inquiries and urgent issues.
What we need to see:
Bachelor's degree or equivalent experience in Engineering, or a related field. An advanced degree or similar experience or relevant certifications are advantageous.
A minimum of 6+ years of experience working in data centers or a similar field.
Proven knowledge of mechanical/electrical systems in infrastructure engineering or management roles.
Solid technical expertise and background in data center systems and processes—power delivery, liquid cooling, rack/server chassis, and cabling.
Proven technical and project leadership under fluid situations, and ability to adapt to change.
Strong communication internally and with customers.
Excellent analytical, problem-solving, communication and decision-making skills, keen attention to detail, and a dedication to quality.
Strong record of excellent partnership and putting the mission's success first.
Coordination & Time Management – proficient at planning, scheduling, and coordinating tasks related to the job to accomplish objectives within or ahead of designated time frames.
Ability to travel (40%).
Way to stand out from the crowd:
Experience in deploying hyperscale data centers, managing operational procedures, and ensuring safety and security protocols.
Sound technical foundation in mechanical and/or electrical systems.
Outstanding social and customer-facing skills.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until July 18, 2026.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.How we rate this
Senior Solutions Architect, First Time Deployment HW/MEP - NVIS at NVIDIA rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in solutions architect jobs at this level
Pay
- This job starts at
- $148k
- Typical solutions architect job
- $169k
This posting starts 12% below the typical solutions architect job. See solutions architect jobs
Questions you could be asked
- Tell me about a project where hardware deployment was part of your work. What did you do?
- Tell me about a project where data center infrastructure was part of your work. What did you do?
- Tell me about a project where power distribution was part of your work. What did you do?
- Tell me about a project where cooling systems was part of your work. What did you do?
- Tell me about a project where system architecture was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Hardware Deployment, Data Center Infrastructure, Power Distribution, Cooling Systems, and System Architecture. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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