Senior Solutions Architect – Customer Success, Partnership
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Job Description
Senior Solutions Architect leading NVIDIA AI infrastructure, networking, and customer success projects. Designing, optimizing, and deploying large-scale GPU-accelerated systems for customers and partners.
Responsibilities:
- Lead hands-on analysis, optimization, and performance tuning of complex GPU-accelerated systems and AI workloads
- Ensure high availability and efficiency across customer data centers
- Serve as a senior technical authority on NVIDIA technologies
- Contribute to architecture reviews and guide infrastructure decisions at scale
- Establish and refine monitoring and optimization methodologies using analytics, telemetry, and automation
- Detect bottlenecks and improve infrastructure resiliency
- Participate in post-deployment reviews and incident retrospectives
- Provide insights into NVIDIA’s infrastructure strategy and help shape the customer experience
- Complete and lead complex technical projects from initial design through implementation and continuous improvement
- Ensure alignment to SLAs and mitigation of technical risks
- Identify AI infrastructure opportunities in cloud and enterprise environments
- Drive technical initiatives showcasing NVIDIA’s leadership
- Interact with customers, partners, and internal teams on large-scale Networking, System Design, and Automation projects
Requirements:
- 10+ years of experience in large-scale data center service operations with a focus on infrastructure
- BS/MS/PhD or equivalent experience in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related fields
- Strong analytical, problem-solving, and decision-making skills
- Strong communication, time management, and organizational skills
- Preferred certifications in data center, server, or networking technologies
- Proficiency in system-level aspects, including Operating Systems, Linux kernel drivers, GPUs, NICs, and hardware architecture
- Expertise in cloud orchestration software and job schedulers, including Kubernetes, Docker Swarm, and Slurm
- Familiarity with cloud-native technologies and integration with traditional infrastructure
- Deep familiarity with AI infrastructure and workflows, including training/inference pipelines, MLOps/DevOps tools, containerization, and large-scale system deployments
- Knowledge of data center infrastructure operations, including safety, security, environmental controls, and standard operating procedures
- Ability to lead discussions, influence outcomes, and build positive relationships with internal and external collaborators
Benefits:
- Willingness to travel up to 25% for customer engagements and team collaboration
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