英伟达Senior Technical Systems AI Architect – Agentic AI

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*Hiring location: Beijing, Shanghai, Guangzhou, Shenzhen, Hong Kong(visa sponsorship provided) Would you like to join one of the fastest-growing teams within Amazon Web Services (AWS) and help shape the future of GPU optimization and high-performance computing? Join us in helping customers across all industries to maximize the performance and efficiency of their GPU workloads on AWS while pioneering innovative optimization solutions. As a Senior Technical Account Manager (Sr. TAM) specializing in GPU Optimization in AWS Enterprise Support, you will play a crucial role in two key missions: guiding customers' GPU acceleration initiatives across AWS's comprehensive compute portfolio, and spearheading the development of optimization strategies that revolutionize customer workload performance. Key Job Responsibilities - Build and maintain long-term technical relationships with enterprise customers, focusing on GPU performance optimization and resource allocation efficiency on AWS cloud or similar cloud services. - Analyze customers’ current architecture, models, data pipelines, and deployment patterns; create a GPU bottleneck map and measurable KPIs (e.g., GPU utilization, throughput, P95/P99 latency, cost per unit). - Design and optimize GPU resource usage on EC2/EKS/SageMaker or equivalent cloud compute, container, and ML services; implement node pool tiering, Karpenter/Cluster Autoscaler tuning, auto scaling, and cost governance (Savings Plans/RI/Spot/ODCR or equivalent). - Drive GPU partitioning and multi-tenant resource sharing strategies to reduce idle resources and increase overall cluster utilization. - Guide customers in PyTorch/TensorFlow performance tuning (DataLoader optimization, mixed precision, gradient accumulation, operator fusion, torch.compile) and inference acceleration (ONNX, TensorRT, CUDA Graphs, model compression). - Build GPU observability and monitoring systems (nvidia-smi, CloudWatch or equivalent monitoring tools, profilers, distributed communication metrics) to align capacity planning with SLOs. - Ensure compatibility across GPU drivers, CUDA, container runtimes, and frameworks; standardize change management and rollback processes. - Collaborate with cloud provider internal teams and external partners (NVIDIA, ISVs) to resolve cross-domain complex issues and deliver repeatable optimization solutions. ------------------------------------------------------

- As an AIML Specialist Solutions Architect (SA) in AI Infrastructure, you will serve as the Subject Matter Expert (SME) for providing optimal solutions in model training and inference workloads that leverage Amazon Web Services accelerator computing services. As part of the Specialist Solutions Architecture team, you will work closely with other Specialist SAs to enable large-scale customer model workloads and drive the adoption of AWS EC2, EKS, ECS, SageMaker and other computing platform for GenAI practice. - You will interact with other SAs in the field, providing guidance on their customer engagements, and you will develop white papers, blogs, reference implementations, and presentations to enable customers and partners to fully leverage AI Infrastructure on Amazon Web Services. You will also create field enablement materials for the broader SA population, to help them understand how to integrate Amazon Web Services GenAI solutions into customer architectures. - You must have deep technical experience working with technologies related to Large Language Model (LLM), Stable Diffusion and many other SOTA model architectures, from model designing, fine-tuning, distributed training to inference acceleration. A strong developing machine learning background is preferred, in addition to experience building application and architecture design. You will be familiar with the ecosystem of Nvidia and related technical options, and will leverage this knowledge to help Amazon Web Services customers in their selection process. - Candidates must have great communication skills and be very technical and hands-on, with the ability to impress Amazon Web Services customers at any level, from ML engineers to executives. Previous experience with Amazon Web Services is desired but not required, provided you have experience building large scale solutions. You will get the opportunity to work directly with senior engineers at customers, partners and Amazon Web Services service teams, influencing their roadmaps and driving innovations.

NVIDIA’s Solution Architect team is looking for a AI-focused Solution Architect with expertise in Large Language Model, generative AI, or recommender system. We work with the most exciting computing hardware and software, driving the latest breakthroughs in artificial intelligence. We need individuals who can enable customer productivity and develop lasting relationships with our technology partners, making NVIDIA an integral part of end-user solutions. We are looking for someone always thinking about artificial intelligence, someone who can maintain constructive collaboration in a fast paced, rapidly evolving field, someone able to coordinate efforts between corporate marketing, industry business development and engineering. You will be working with the latest AI architecture coupled with the most advanced neural network models, changing the way people interact with technology.As a Solutions Architect, you will be the first line of technical expertise between NVIDIA and our customers. Your duties will vary from working on proof-of-concept demonstrations, to driving relationships with key executives and managers to evangelize accelerated computing. Dynamically engaging with developers, scientific researchers, data scientists, IT managers and senior leaders is a meaningful part of the Solutions Architect role and will give you experience with a range of partners and concerns. What you’ll be doing: • Assisting field business development in guiding the customer build/extend their GPU infrastructures for AI. • Help customers build their large-scale projects, especially Large Language Model (LLM) projects. • Engage with customers to perform in-depth analysis and optimization to ensure the best performance on GPU architecture systems. This includes support in optimization of both training and inference pipelines. • Partner with Engineering, Product and Sales teams to develop, plan best suitable solutions for customers. Enable development and growth of product features through customer feedback and proof-of-concept evaluations. • Build industry expertise and become a contributor in integrating NVIDIA technology into Enterprise Computing architectures.