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Description and Requirements

职责：

1. 负责AI产品的架构安全审查。
2. 参与制定AI产品的架构设计安全要求、架构设计规范以及相应的审计方法和检查清单。
3. 研究并应用最新的大型语言模型微调技术，以提升模型性能，特别是在辅助威胁分析和安全架构设计方面。
4. 探索并整合检索增强生成（RAG）原理及相关技术，以增强模型的知识获取能力，并确保信息的准确性和完整性。
5. 开发并优化基于AI代理的内容处理流程，以支持复杂的信息安全任务。
6. 与团队成员紧密合作，解决开发过程中遇到的技术问题，确保产品架构和解决方案的安全性。
7. 跟踪人工智能和信息安全领域的最新研究进展，并将其应用于产品中。

资格要求：

1. 熟悉SDL流程和DevSecOps最佳实践。
2. 拥有计算机科学、信息安全或相关领域的学士学位或以上学历。
3. 深入了解深度神经网络的基本原理，包括卷积神经网络（CNN）、循环神经网络（RNN）等。
4. 精通大型语言模型的微调技术，能够根据应用场景调整模型参数以优化性能。
5. 熟悉检索增强生成（RAG）的原理，并了解如何将其应用于主流AI应用中。
6. 在LangChain和Agent等内容处理流程方面有研究经验。
7. 精通PyTorch或TensorFlow中的至少一个框架，并具备实际的模型训练和微调经验者优先。
8. 具备良好的编程技能和算法基础，并熟悉Python编程语言。
9. 具备良好的沟通能力和团队合作精神。
10. 英语读写能力良好的候选人优先考虑。

Responsibilities:

1. Responsible for the architectural security review of AI products.
2. Participate in the preparation of architectural design security requirements, architectural design specifications and supporting audit methods and CheckLists for AI products.
3. Research and apply the latest large language model fine-tuning technology to improve model performance, especially in assisting threat analysis and security architecture design.
4. Explore and integrate RAG principles and related technologies to enhance the knowledge acquisition ability of the model and ensure the accuracy and completeness of information.
5. Develop and optimize AI Agent-based content processing processes to support complex information security tasks.
6. Work closely with team members to solve technical problems encountered during development and ensure the security of product architecture and solutions.
7. Follow the latest research progress in artificial intelligence and information security and apply it to products.

Qualifications:
1. Familiar with SDL processes and DevSecOps best practices.
2. Bachelor's degree or above in computer science, information security or related fields.
3. deep understanding of the basic principles of deep neural networks, including convolutional neural networks (CNN), recurrent neural networks (RNN), etc.
4. Master the fine-tuning technology of large language models and be able to adjust model parameters according to application scenarios to optimize performance.
5. Familiar with the principles of retrieval augmented generation (RAG) and understand how to apply it to mainstream AI applications.
6. Have research experience in content processing processes such as LangChain and Agent.
7. Master at least one of the PyTorch or TensorFlow frameworks, and have practical model training and fine-tuning experience is preferred.
8. Have good programming skills and algorithm foundation, and be familiar with the Python programming language.
9. Good communication skills and teamwork spirit.
10. Candidates with good English reading and writing skills are preferred.

Additional Locations:
* China - Beijing - 北京（Beijing）