NVIDIA
AI
SeniorSolutionsArchitect,AIHyperscalers
“Senior Solutions Architect, AI Hyperscalers at NVIDIA. Skills: AI/ML Solutions Architecture, Hyperscale customer engagement, Cloud Service Provider engagement, AI training and inference infrastructure, Python, Linux. Lead software customer technical engagement for AI training, inference and infrastructure being deployed at vast scale. Work across multiple organizations within NVIDIA as well as at the customer to ensure successful and trouble-free deployments”
What You'll Achieve.
ensure successful and trouble-free deployments; create a robust large scale artificial intelligence infrastructure; secure design wins; bring solutions to production; support them throughout their lifecycle; enhance the value of NVIDIA technology; mitigate risks
Industry & Context.
passion for problem-solving; Facilitate the resolution of customer issues
What They're Looking For.
Must Have
BS/MS in Computer Science, Electrical Engineering, or equivalent experience, 8+ years of engineering experience with a proven track record in AI/ML-focused projects or enterprise-grade solutions, Proven understanding of Linux, including solving, optimization, and customization for AI/ML workloads, understanding of data science and machine learning infrastructure—software and hardware, Professional-level communication skills, including the ability to tailor messages for varying technical audiences and maintain composure in high-pressure situations, Excellent follow-up and interpersonal skills, with a true passion for problem-solving, Proficient in Python, with the ability to develop scripts and build custom tools, Shown eagerness to learn and apply new technologies
Nice to Have
Experience with Chatbots, RAG pipelines, vector databases, and distributed training or inference workloads, Experience or background in HPC (High Performance Computing) environments for AI or ML applications, Familiarity with multi-node GPU clusters and performance tuning for large-scale AI workloads, Experience developing in cloud and/or virtualized environments, containerized solutions, with knowledge of Docker, Kubernetes, Background with common deep learning frameworks such as PyTorch or JAX, Experience with parallel programming or GPU acceleration (e. g. , CUDA) is helpful
What You'll Do.
Lead software customer technical engagement for AI training
inference and infrastructure being deployed at vast scale
Work across multiple organizations within NVIDIA as well as at the customer to ensure successful and trouble-free deployments
Partner with a large company to build automation and management to create a robust large scale artificial intelligence infrastructure
Optimization and characterization of customer specific AI models and pipelines
Serve as the main point of contact for NVIDIA products
enabling internet giants and cloud providers to have an innovative AI/ML software infrastructure
Work directly with best-in-class engineering teams to secure design wins
bring solutions to production
and support them throughout their lifecycle
Become a trusted advisor to your customer by understanding their environment
and long-term strategy
Translate these insights into product requirements and innovative solutions
Help your customer enhance the value of NVIDIA technology
and provide feedback to NVIDIA for future product improvements
Facilitate the resolution of customer issues
offering timely and proactive communications to mitigate risks
and proof-of-concepts to showcase NVIDIA’s AI/ML capabilities
Guide customers on standard processes for scalable AI model deployment and inference optimization
How You'll Work.
Team & Collaboration
Work across multiple organizations within NVIDIA; Work with customers; Partner with a large company; Work directly with best-in-class engineering teams; Serve as a key technical member of a focused account team
Communication Scope
Professional-level communication skills; ability to tailor messages for varying technical audiences; maintain composure in high-pressure situations; timely and proactive communications
Applying for this Senior Solutions Architect, AI Hyperscalers role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Workday
- Workday has a multi-step form — save your progress after every section.
- "Apply With LinkedIn" can fail or lose data; manual entry is more reliable.
- Watch for the "Submit for Review" final step — hitting "Save" alone does not submit.
- Job requisition numbers are useful when following up with HR by email.
ANONYMOUS · UNFILTERED
What do employees actually say about NVIDIA?
Real rants from real employees. Read before you apply.