基本信息

职位编号:
100016052
工作领域:
Artificial Intelligence
国家/地区:
中国
省:
北京
市:
北京（Beijing）
日期:
星期四, 6 月 5, 2025
其他工作城市: 
* China - Beijing - Beijing
* China - Guangdong - Shenzhen
* China - Tianjin - Tianjin

为什么选择联想

联想文化，我们称之为 “We Are Lenovo”（我们，就是联想），其核心是：“说到做到，尽心尽力，成就客户”。

联想集团是一家年收入569亿美元的全球化科技巨头，位列《财富》世界500强第248名，服务遍布全球180个市场数以百万计的客户。为实现“智能，为每一个可能” 的公司愿景，联想在不断夯实全球个人电脑市场冠军地位的基础上，积极构建全栈式的计算能力，现已拥有包括人工智能赋能、人工智能导向和人工智能优化的终端、基础设施、软件、解决方案和服务在内的完整产品路线图，包括个人电脑、工作站、智能手机、平板电脑等终端产品，服务器、存储、边缘计算、高性能计算以及软件定义等基础设施产品。这一变革与联想改变世界的创新一起，共同为世界各地的人们成就一个更加包容、值得信赖的智慧未来。联想集团有限公司在香港交易所上市（港交所：992）（美国预托证券代号：LNVGY）。

欢迎访问联想官方网站 https://www.lenovo.com，并关注“联想集团”微博及微信公众号等社交媒体官方账号，或关注“联想招聘”公众号，获取联想最新动态。

职位描述和要求:

Job Responsibilities:
1.As a technical leader for Global Supply Chain AI transformation, design and implement AI, AI Agent technical frameworks leveraging corporate level LLM and Agent builder.
2.Evaluate current supply chain use cases and design AI Agent related solution as technical owner.
3.Leading to build domain agents/solutions, with knowledge of frameworks like LangChain, ReAct, or LangGraph, orchestrate LLMs, external APIs, and databases.
4.Tune LLM leveraging prompt, RAG, few-shot and related methods
5.Lead to implement tools to extend LLM functionality and capability to enhance agent
6.Ensure solutions and deliverables align with business objectives and security/compliance.
7.Measure the effectiveness of AI solution and make necessary adjustments.

Job Requirements:
1.Major in Computer Science, AI, or related fields.
2.Proven work experience as an AI Architect or AI Senior Developer
3.Deep understanding of architectures for AI Agent systems, integrating components such as planning, memory (short/long-term), tool usage, and feedback loops
4.Hands-on experience with LangChain, LlamaIndex, or similar LLM frameworks
5.Strong Architecture and programming skills (at least one of programming language Python/Java//Go )
6.6+ years in AI solution/Agent engineering, or deep experience in Agentic workflow engineering.
7.Experience in performance optimization and efficiency improvement of deep learning models is preferred
8. Open to Several Locations: Beijing, Tianjin, Shenzhen

其他工作城市: 
* China - Beijing - Beijing
* China - Guangdong - Shenzhen
* China - Tianjin - Tianjin
* China - Beijing - Beijing , * China - Guangdong - Shenzhen , * China - Tianjin - Tianjin
* China - Beijing , * China - Guangdong , * China - Tianjin
* China
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