AMDAI Solution Architect (GPU)

THE ROLE: “AI Product Applications Engineer (Solution Architect) – China” position is in the AMD AI group, located in China. THE PERSON: Success in this role will require deep knowledge of Data Center, Client, Endpoint AI workloads such as LLM, Generative AI, Recommendation, and/or transformer … AI cross cloud, client, edge… the candidate needs to have hands-on experiences with various AI models, end-to-end pipeline, industry framework (pytrouch, vLLM, SGLang, llm-d,Triton) / SDKs and solutions. KEY RESPONSIBILITIES: Position technical proposals / enablement to (blogs, tutorials, user guide…) AI SW developers and/or top customers. Provide significant contribution to AI SW developers / communities and/or customer PoC success. Drive AI developers / communities / customer requirements for AI SW, solution roadmap planning. Analyze competitive solutions to identify strength and weaknesses for articulating AMD AI SW & solution value propositions. Provide inputs / feedback to AI SW / hardware silicon / board roadmap for AI cross cloud, client, and edge...

- 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.

Responsibilities Collaborate with GPU sales team and SCE AIML TPM team to provide technical support for customers both at pre-sales and after-sales stage. Take ownership of problems and work to identify solutions. Design, deploy, and manage infrastructure components such as cloud resources, distributed computing systems, and data storage solutions to support AI/ML workflows. Collaborate with customers’ scientists and software/infrastructure engineers to understand infrastructure requirements for training, testing, and deploying machine learning models. Implement automation solutions for provisioning, configuring, and monitoring AI/ML infrastructure to streamline operations and enhance productivity. Optimize infrastructure performance by tuning parameters, optimizing resource utilization, and implementing caching and data pre-processing techniques. Troubleshoot infrastructure performance, scalability, and reliability issues and implement solutions to mitigate risks and minimize downtime. Stay updated on emerging technologies and best practices in AI/ML infrastructure and evaluate their potential impact on our systems and workflows. Document infrastructure designs, configurations, and procedures to facilitate knowledge sharing and ensure maintainability. Qualifications: Experience in scripting and automation using tools like Ansible, Terraform, and/or Kubernetes. Experience with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools for managing distributed systems. Solid understanding of networking concepts, security principles, and best practices. Excellent problem-solving skills, with the ability to troubleshoot complex issues and drive resolution in a fast-paced environment. Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams and convey technical concepts to non-technical stakeholders. Strong documentation skills with experience documenting infrastructure designs, configurations, procedures, and troubleshooting steps to facilitate knowledge sharing, ensure maintainability, and enhance team collaboration. Strong Linux skills with hands-on experience in Oracle Linux/RHEL/CentOS, Ubuntu, and Debian distributions, including system administration, package management, shell scripting, and performance optimization.