Job Description:GPU AI solution architect is the curator and owner of the AI architecture strategy of account engagement. Who is the technical glue between account team, AI Business Acceleration and Engineering Solutions Manager (AI BEM), Product Line Manager, Technical Program Manager, to govern and scale the AI initiatives of AXG China. What you will do: Intel GPU has a diverse range ofAI use casesand deployment models, so AI solutions architect need a wide array of capabilities: Discuss the feasibility of use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation. At the same time, bring attention to misaligned initiatives and impractical use cases. Align technical implementation with existing and future requirementsby gathering inputs from multiple stakeholders - account team, product manager, security professionals, engineering leaders, and those in global solutions architect peer groups - and developing processes and products based on the inputs. Play a key role in defining the AI architecture and selecting appropriate GPU and software technologies from a pool of open-source and commercial offerings. Select cloud, on-premises or hybrid deployment models, and ensure new tools are well-integrated with existing data management and analytics tools. Work closely with security and risk leaders to foresee and overturn risks,such as training data poisoning, AI model theft and adversarial samples, ensuringethical AI implementation and restoring trust in AI systems. Remain acquainted with upcoming regulations and map them to best practices. Technical skills you bring: AI architecture and pipeline planning. Understand the workflow and pipeline architectures ofaccount deep learningworkloads. An in-depth knowledge of components and architectural trade-offs involved across the silicon, model building, deployment and production workflows of AI is a must. DL workload optimization engineering planning,including knowledge of layer fusion, tensor fusion, kernel autotuning, quantization. The goal is to speed up time, reduce latency, increase hardware utilization. Software engineering know-how,including knowledge of DevOps workflows and tools, such as Git, containers, Kubernetes and CI/CD, ML and Deep Learning frameworks (such as TensorFlow) and ML techniques (such as random forest and neural networks). Non-technical skills you bring: Thought leadership.Be change agents to help the customers to adopt an AI-driven mindset based on Intel GPU product offerings. Take a pragmatic approach to the limitations and risks of AI, and project a realistic picture in front of IT executives who provide overall digital thought leadership. Collaborative mindset.To ensure that AI platforms deliver both business and technical requirements, seek to collaborate effectively with data scientists, data engineers, data analysts, ML engineers, other architects, business unit leaders and CxOs (technical and nontechnical personnel), and harmonize the relationships among them.Qualifications:Qualifications: Master of PhD degree in EE/CS and related discipline 15 years of experience in accelerated computing domain with capacity of product planning, software engineering, or RnD Experience with AI architect design, which includes but not limited to envision, build, deploy and operationalize an end-to-end machine learning (ML) and AI pipeline Highly desirable with silicon RnD experience, system development and AI software engineering Highly desirable hands-on AI software stack RnD experience in speed up time, reduce latency, increase hardware utilization. Highly desirable knowledge into PRC accelerators competition landscape and major players product offerings 职能类别：芯片架构工程师