基本信息

职位编号:
100016367
工作领域:
Engineering
国家/地区:
中国
省:
天津
市:
天津(Tianjin)
日期:
星期四, 10 月 23, 2025
其他工作城市
* China

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690亿美元的全球化科技巨头,位列《财富》世界500强第196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

Maintaining and improving the existing system to meet complexed and rapidly changed business scenario. 

Establish the AI center to utilize AI technology for business functions such as demand forecasting, inventory monitoring, and process automation.

Review business requirement document and develop automation solutions by using Java / SQL and other similar programming languages.

Job Requirement:

Bachelor or above Degree in Computer Science, Computer or Software Engineering, or related field. 5+ years of experiences AI or Java software development. Responsible for designing innovative and highly efficient AI system architectures tailored to complex business scenarios, utilizing AI algorithms to enhance the system's decision-making capabilities and prediction accuracy, thereby meeting diverse business needs. Good communication skills, good language skills in English. Proficient in Python and familiar with PyTorch/TensorFlow frameworks.

Understanding of fundamental data structures and algorithms, with strong coding standards.

Basic Java coding skills are preferred.

Solid understanding of machine learning (e.g., classification/clustering) and deep learning (e.g., RNN/Transformer) theories. Understanding of basic Agent architectures (e.g., task planning, memory management, tool invocation). Familiarity with frameworks such as LangChain and AutoGPT is preferred. Knowledge of RAG (Retrieval-Augmented Generation), MCP/A2A multi-agent collaboration technologies. Familiarity with mainstream large models (e.g., GPT, DeepSeek, LLaMA, ERNIE Bot) and fine-tuning methods. Basic experience in Prompt Engineering, capable of optimizing model outputs through prompt tuning. Understanding of lightweight model deployment (e.g., OLLMA, model quantization).

其他工作城市
* China
* China