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Open nowPosted 74 days ago

【中台】后端开发工程师（AI Native 方向）

moonton/index132 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now【中台】后端开发工程师（AI Native 方向）moonton/index · Shanghai, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on moonton/index's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. moonton/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 74 days ago

moonton/index median: 1 days open

The posting

负责沐瞳 AI Native 产品及 WEB 平台相关服务的架构设计、研发交付与持续优化。岗位以后端技术栈为核心，采用端到端研发模式，不设置前后端职责边界，围绕产品目标完成从需求理解、方案设计、服务开发、AI 能力集成到上线运营的完整研发流程。 1. 负责 WEB 后台服务的架构设计、功能开发及持续优化，建设稳定、高性能、可扩展的服务体系； 2. 深入理解业务需求和产品设计，参与需求分析、技术方案评审，与产品、设计、测试等团队协作，高质量完成产品交付； 3. 参与核心系统和基础服务建设，解决高并发、高可用、系统解耦、性能优化等技术挑战； 4. 具备端到端研发能力，根据业务需求完成服务端、前端页面及相关工程能力建设，提升研发交付效率； 5. 探索 AI 技术在业务和研发场景中的应用，推动 Agent、Workflow、RAG、知识库等 AI 能力落地； 6. 使用 Cursor、Claude Code、Copilot 等 AI 工具提升需求分析、方案设计、编码开发、测试验证和文档沉淀效率，推动研发流程智能化升级； 7. 关注 AI 应用效果评测、系统稳定性、可观测性和成本优化，推动 AI 能力从验证阶段走向生产应用； 8. 快速定位和解决线上问题，持续优化服务性能、工程质量和系统可靠性。

1. 计算机或相关专业本科及以上学历，具备扎实的数据结构、算法、操作系统、计算机网络和数据库基础； 2. 具备 3年以上服务端研发经验，熟悉 Golang，具备良好的工程设计能力和编码能力； 3. 熟悉 Web 服务开发，掌握 API 设计规范，具备微服务、分布式系统、高并发服务开发经验； 4. 熟悉 MySQL、Redis 等常用数据库和缓存组件，理解数据存储、索引、事务、缓存等基本原理； 5. 熟悉 Kafka、MongoDB、Elasticsearch 等常用中间件，有实际应用和性能优化经验者优先； 6. 了解 Linux 系统、Shell 脚本、Docker、Kubernetes 等云原生相关技术； 7. 具备端到端研发能力，能够完成从后端服务到业务页面的完整功能交付； 8. 对 AI Native 应用开发有兴趣，愿意探索大模型技术在业务和研发场景中的应用； 9. 熟悉 AI 编程工具，并具备利用 AI 提升研发效率的实践经验； 10. 具备良好的问题分析能力、沟通协作能力和自驱学习能力，能够主动发现问题并推动解决。

--- AI 方向加分项 具备以下经验者优先： 1. 有大模型应用开发经验，包括 Agent、RAG、Workflow、知识库等方向； 2. 理解 Prompt Engineering、Context Engineering、Tool Calling、Memory 等 AI 应用核心概念； 3. 有 MCP（Model Context Protocol）、Multi-Agent、多模态 AI 应用实践经验； 4. 有 AI 平台、Agent 平台、企业知识库、研发提效平台或 AI 基础设施建设经验； 5. 有 AI 应用评测（Evaluation）、可观测性（Observability）、成本治理（Cost Governance）相关经验； 6. 有从 0 到 1 构建 AI Native 产品并投入生产使用经验。

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