Nvidia Senior Solutions Architect NVIS Customer Success Partnership 

Singapore, Singapore 

874223474

What you'll be doing:

• Lead the hands-on analysis, optimization, and performance tuning of complex GPU-accelerated systems and AI workloads, ensuring high availability and efficiency across customer data centers.
• Serve as a senior technical authority on NVIDIA technologies, contributing to architecture reviews and guiding infrastructure decisions at scale.
• Establish and refine monitoring and optimization methodologies using analytics, telemetry, and automation to detect bottlenecks and improve infrastructure resiliency.
• Join post-deployment reviews, incident retrospectives, and sessions to craft the customer experience and provide insights into NVIDIA’s infrastructure strategy.
• Complete and lead complex technical projects from initial design through implementation and continuous improvement, ensuring alignment to SLAs and mitigation of technical risks.
• Support business growth by identifying AI infrastructure opportunities in cloud and enterprise environments and driving technical initiatives that showcase NVIDIA’s leadership in this space.

What we need to see:

• 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, solving problems, and decision-making skills, capable of identifying root causes, driving continuous improvement, and delivering resilient technical solutions.
• Strong communication, time management, and organizational skills, with the ability to lead complex projects, guide technical teams.
• Preferred certifications in data center, server, or networking technologies, and a willingness to travel up to 25% for customer engagements and team collaboration.
• Proficiency in system-level aspects, encompassing Operating Systems, Linux kernel drivers, GPUs, NICs, and hardware architecture.
• Shown expertise in cloud orchestration software and job schedulers, including platforms like Kubernetes, Docker Swarm, and HPC-specific schedulers such as Slurm.
• Familiarity with cloud-native technologies and their integration with traditional infrastructure is essential.

Ways to stand out from the crowd:

• Deep familiarity with AI infrastructure and workflows, including training/inference pipelines, MLOps/DevOps tools, containerization (Docker, Kubernetes), and large-scale system deployments.
• Knowledge of data center infrastructure operations, including safety, security, environmental controls, and standard operating procedures.
• Good interpersonal and collaboration skills, with the ability to lead discussions, influence outcomes, and build positive relationships with both internal and external collaborators.