锦鲤求职岗位描述
岗位描述
1、作为 AI 基础设施解决方案架构师，深入理解 GPU 智算集群及云上 AI 基础设施的技术原理、产品架构和使用场景，能够根据企业级客户的业务目标、模型规模及训练与推理需求，规划设计高性能、高可靠、可扩展的端到端解决方案，推动 AI 基础设施从技术验证到商业化落地。
2、具备良好的架构思维和系统优化能力，能够从稳定性、高性能、易用性、可用性、可运维性及成本效益等方面综合考虑，完成从算力规划、集群组网、高性能网络、存储体系到训练与推理服务部署的全链路架构设计；深入客户生产环境，围绕训练效率、推理时延、吞吐、算力利用率及单位调用成本等指标开展性能瓶颈分析与调优，输出可量化的优化结果。
3、与前线架构师、业务拓展团队紧密合作，主动了解客户在 AI 基础设施产品和方案中遇到的困难与需求，主导技术交流、方案设计、POC、基准测试及关键项目交付，帮助客户解决技术问题并识别新的业务突破点；能够影响客户的基础设施、工程、平台及算法等不同团队，用产品和技术推动业务扩展。
4、分析 AI 与云计算技术发展趋势、GPU 资源供需、行业应用演进及市场竞争格局，挖掘客户商机并形成差异化定位；沉淀可复制的行业解决方案、最佳实践，与产品团队、资源及经营、研发和交付团队紧密配合，持续反哺产品能力建设，提升产品在海外市场竞争力并推动营收增长。
1. As an AI Infrastructure Solutions Architect, develop a deep understanding of the technical principles, product architectures, and use cases of GPU computing clusters and cloud-based AI infrastructure. Based on enterprise customers’ business objectives, model scale, and training and inference requirements, design high-performance, highly reliable, scalable, end-to-end solutions and drive AI infrastructure initiatives from technical validation through commercial deployment.
2. Apply strong architectural thinking and system optimization capabilities across reliability, performance, usability, availability, operability, and cost efficiency. Design full-stack architectures covering compute capacity planning, cluster networking, high-performance networking, storage systems, and training and inference service deployment. Work within customer production environments to identify and resolve performance bottlenecks, using metrics such as training efficiency, inference latency, throughput, compute utilization, and cost per inference request to deliver measurable optimization results.
3. Work closely with field solution architects and business development teams to understand customers’ challenges and requirements for AI infrastructure products and solutions. Lead technical engagements, solution design, POCs, benchmarking, and critical project delivery; resolve technical issues, identify new business opportunities, and influence customer infrastructure, engineering, platform, and algorithm teams to drive business expansion through products and technology.
4. Analyze trends in AI and cloud computing, GPU supply and demand, evolving industry use cases, and the competitive landscape to identify customer opportunities and develop differentiated positioning. Build reusable industry solutions and best practices. Collaborate closely with product teams, capacity and business operations, R&D, and delivery teams to continuously feed insights back into product capability development, strengthen product competitiveness in overseas markets, and drive revenue growth.
岗位要求
1、计算机科学、电子工程或相关领域本科及以上学历，具有 5 年以上云计算平台、分布式系统、大规模数据处理、AI 基础设施或人工智能系统架构设计与实施经验。
2、深入理解 AI/ML 技术栈，熟悉 PyTorch、TensorFlow 等深度学习框架以及大模型训练和推理的原理与流程；熟悉 DeepSpeed、Megatron-LM 等分布式训练框架及 vLLM、TensorRT-LLM 等推理框架，具备训练或推理性能优化的实际经验。有成功主导大型 AI 项目或产品从 0 到 1 设计与实施经验者优先。
3、精通分布式计算及云原生架构，熟悉 Docker、Kubernetes 等容器化和编排技术；深入了解 GPU、DPU、高性能网络（如 RoCE、InfiniBand）及高性能存储原理，具备大规模 GPU 集群建设、管理或性能调优经验。熟练掌握 Python 等编程语言者优先。
4、熟悉阿里云、AWS、GCP、Azure 等主流云平台，具备云上高可用、高扩展 AI 智算集群的设计和部署经验；能够围绕工作负载特征开展芯片与实例选型、容量规划、网络拓扑和存储方案设计，并在性能、成本和交付效率之间作出合理权衡。熟悉阿里云 PAI、GPU 实例、CPFS 等产品者优先。
5、具备优秀的沟通协调、方案撰写和演讲能力，能够面向客户技术团队、管理层及内部协作团队清晰传达技术决策、架构设计与业务价值；
6、英语须能够作为日常工作语言，并具备良好的跨区域、跨文化协作能力。有海外留学或工作经历，有云产品售前、POC、大型客户项目经验者优先。
7、对 AI 基础设施技术有强烈热情和好奇心，自驱力和学习力强；具备良好的市场敏感度、分析与解决问题能力、团队协作能力和结果意识，善于攻坚复杂问题，能够在快速变化的技术和业务环境中持续推动问题解决与项目落地。
1. Bachelor’s degree or above in Computer Science, Electronic Engineering, or a related field, with 5+ years of experience in cloud platforms, distributed systems, large-scale data processing, AI infrastructure, or the architecture and implementation of AI systems.
2. Deep understanding of the AI/ML technology stack, including deep learning frameworks such as PyTorch and TensorFlow and the principles and workflows of large-model training and inference. Familiarity with distributed training frameworks such as DeepSpeed and Megatron-LM and inference frameworks such as vLLM and TensorRT-LLM, with hands-on experience in training or inference performance optimization. Experience leading the design and implementation of a large-scale AI project or product from zero to one is preferred.
3. Strong expertise in distributed computing and cloud-native architectures, with proficiency in containerization and orchestration technologies such as Docker and Kubernetes. Deep understanding of GPUs, DPUs, high-performance networking such as RoCE and InfiniBand, and high-performance storage. Experience building, managing, or optimizing large-scale GPU clusters is required. Proficiency in programming languages such as Python is preferred.
4. Familiarity with major cloud platforms such as Alibaba Cloud, AWS, GCP, and Azure, with experience designing and deploying highly available and scalable AI computing clusters in the cloud. Ability to perform chip and instance selection, capacity planning, network topology design, and storage architecture based on workload characteristics, balancing performance, cost, and delivery efficiency. Experience with Alibaba Cloud products such as PAI, GPU instances, and CPFS is preferred.
5. Excellent communication, coordination, proposal writing, and presentation skills, with the ability to clearly communicate technical decisions, architectural designs, and business value to customer technical teams, executives, and internal stakeholders.
6. English proficiency sufficient for use as a day-to-day working language is required, along with the ability to collaborate effectively across regions and cultures. Overseas study or work experience, experience working with foreign-owned or multinational enterprise customers, or experience in cloud pre-sales, POCs, or projects for major enterprise customers is preferred.
7. Strong passion and curiosity for AI infrastructure technologies, with a high degree of self-motivation and learning agility. Strong market awareness, analytical and problem-solving skills, teamwork, and results orientation. Ability to tackle complex technical challenges and continuously drive issue resolution and project delivery in a fast-changing technology and business environment.