存储云平台研发（数据库智能化方向）- 国际化

1、主导存储云平台架构设计与核心开发：负责小红书存储平台产品的架构演进与核心模块开发，探索AI-Native架构下的智能存储解决方案，推动平台从工具型向智能型升级。 2、构建智能化运维体系：独立负责存储与数据库自助管理平台的技术规划与落地，结合大模型能力打造智能运维Agent，实现自然语言交互式运维，降低数据库使用门槛。 3、驱动运维智能化转型：基于LLM Agent技术重构自动化运维平台，构建具备自诊断、自修复、自优化能力的智能运维体系，实现从"人工运维"到"无人值守"的跨越。 4、深耕AI+数据库创新场景：主导大模型在数据库领域的深度应用落地，包括智能SQL优化、异常检测与根因分析、容量预测与自动扩缩容、智能告警降噪等核心场景。 5、引领产品体验升级：持续跟踪业界前沿AI Agent产品（如OpenClaw、Claude Code、Devin等），将先进AI交互范式融入数据库存储平台，打造行业标杆级智能化运维体验。 1、Lead Storage Cloud Platform Architecture & Core Development — Drive the architectural evolution and core module development of Rednote's storage platform. Explore AI-Native architectures for intelligent storage solutions and transform the platform from a tooling-focused system into an intelligent platform. 2、Build Intelligent Operations System — Own the technical planning and execution of a self-service storage and database management platform. Leverage large language model capabilities to build an intelligent operations agent that supports natural language interaction, lowering the barrier to database operations. 3、Drive Intelligent Operations Transformation — Re-architect the automated operations platform based on LLM Agent technologies to build a self-diagnosing, self-healing, and self-optimizing intelligent operations system — enabling a shift from manual operations to fully automated, zero-touch operations. 4、Deepen AI + Database Innovation — Lead practical LLM adoption in database domains, including: intelligent SQL optimization, anomaly detection and root cause analysis, capacity prediction and auto-scaling, and intelligent alerting noise reduction. 5、Elevate Product Experience — Continuously track leading AI Agent products (such as Cursor, Claude Code, Devin, etc.) and integrate advanced AI interaction paradigms into the database storage platform to deliver industry-leading intelligent operations experience.

1、扎实的工程基础：精通Go/Java/Python至少一门后端语言，熟悉Vue/React等前端框架，具备基于大模型API（OpenAI、Claude、通义千问等）开发Agent应用的实战经验。 2、深入的存储领域经验：对分布式存储产品有深度实践经验，熟悉分布式KV/缓存、MySQL、分布式数据库、图数据库、表格存储、对象存储等至少两种以上系统原理及运维实践。 3、AI应用开发能力：熟练掌握大模型应用开发技术栈，包括RAG、Prompt Engineering、Function Calling、多Agent协作框架（LangChain、LlamaIndex、AutoGen等），有完整项目落地经验。 4、产品思维与AI敏感度：对OpenClaw、Claude Code、Cursor、GitHub Copilot等AI工具有深入理解和实践经验，具备将AI能力转化为产品功能的设计思维和工程落地能力。 5、优秀的软实力：具备出色的需求拆解和技术攻坚能力，擅长跨团队沟通协作，有良好的客户服务意识和Ownership精神，对AI技术在基础设施领域的应用有前瞻性判断。 6、加分项：有技术博客、开源项目贡献、AI相关竞赛获奖经历；有AIOps、智能运维、LLM Agent相关领域项目经验者优先。 1、Strong Engineering Foundation — Proficient in at least one backend programming language (Go / Java / Python); familiar with frontend frameworks (Vue / React); hands-on experience building Agent applications using large model APIs (OpenAI, Claude, Qwen, etc.). 2、Deep Storage Domain Experience — Hands-on experience with distributed storage; familiar with the principles and operational practices of at least two of: distributed KV/cache, MySQL, distributed databases, graph databases, table storage, or object storage. 3、AI Application Development — Proficient in the LLM application development stack: RAG, Prompt Engineering, Function Calling, and multi-agent frameworks (LangChain, LlamaIndex, AutoGen, etc.); experience shipping complete projects. 4、Product Thinking & AI Fluency — Deep practical experience with AI tools such as Cursor, Claude Code, GitHub Copilot, etc.; ability to translate AI capabilities into product features and engineering delivery. 5、Strong Soft Skills — Excellent at breaking down requirements and driving technical execution; strong cross-team communicator; customer-oriented mindset and strong sense of ownership; forward-looking judgment on AI in infrastructure; fluent in both English and Chinese (spoken and written). 6、Bonus — A technical blog, open-source contributions, or AI competition awards are a plus; hands-on experience in AIOps, intelligent operations, or LLM Agent development is highly preferred. 我们提供： 1、参与小红书亿级流量存储平台建设的实战机会 2、与顶尖AI和基础设施团队合作的技术氛围 3、在AI+数据库前沿领域的深度探索空间