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Open nowPosted 255 days ago

AI工程师 - Agent

kaonai/index37 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAI工程师 - Agentkaonai/index · Beijing, Chinese Mainland

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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.

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 255 days ago

The posting

负责公司AI Agent系统的工程设计、开发与落地，围绕个性化内容生成与长期记忆场景，端到端负责Agent从原型到生产的完整生命周期（Agent Development Life Cycle），具体职责包括但不限于：

1. Agent系统架构：设计并实现生产级Agent运行框架，包括编排引擎（Orchestration）、工具调用链路（Tool Use Pipeline）、记忆存储与检索系统、上下文管理等核心模块；确保系统在高并发场景下的稳定性、低延迟和可扩展性 2. Agent行为工程：设计并实现Agent的编排策略、路由逻辑、多步规划与fallback机制；将算法团队的研究成果（记忆、个性化、持续学习等）工程化为可靠的生产行为模式，确保Agent在真实场景中的可控性与一致性 3. 记忆系统工程：构建Agent长期记忆的存储、索引和检索基础设施；实现高效的向量检索、记忆版本管理、增量更新与过期淘汰机制；支撑sleep-time异步记忆整理的后台任务调度与计算资源管理 4. 工具调用与环境集成：设计并实现Agent与外部工具、API、数据源的集成框架；构建安全、可靠的工具调用沙箱与执行环境；实现工具注册、权限管理、调用链追踪与异常处理等工程能力 5. 可观测性、评估与持续改进：构建Agent行为的全链路追踪、日志与监控体系；设计Agent质量评估pipeline，包括个性化效果、记忆准确性、多轮一致性等维度的自动化评测；建立从发布、灰度、A/B测试到反馈收集的完整迭代流程，通过线上数据分析定位Agent薄弱环节，形成"发布→度量→归因→改进"的持续优化飞轮 6. 推理与服务优化：优化Agent推理链路的端到端性能，包括LLM调用策略（缓存、批处理、流式输出）、上下文窗口高效利用、多轮对话状态管理等；针对异步Agent场景设计高效的任务队列与调度系统 7. 数据平台与Pipeline：构建Agent训练数据的采集、清洗、标注与管理平台；设计用户交互日志的实时/离线处理pipeline，为算法团队的SFT、RL训练和奖励模型迭代提供高质量数据支撑 8. 算法-工程协作：与算法团队紧密配合，理解记忆、持续学习、个性化奖励模型等研究方向的技术原理，将研究成果高效转化为可靠的生产系统；共同定义Agent评估标准与数据需求，打通从研究到上线的协作链路

1. 扎实的软件工程能力，熟练掌握Python，具备大规模分布式系统的设计与开发经验；熟悉Go、Rust、C++中至少一门者优先； 2. 对LLM-based Agent系统有深入理解，具备Agent系统的生产环境开发与部署经验； 3. 具备良好的系统设计能力，能在可靠性、延迟、成本之间做出合理权衡；有消息队列、任务调度、异步计算等基础设施经验者优先； 4. 对Agent记忆机制、持续学习、个性化系统等方向有技术热情，能理解算法原理并将研究成果转化为生产系统； 5. 具备较强自驱力和ownership意识，能端到端拥有Agent从设计、开发、部署到持续迭代的完整流程。

加分项： 1. 具备向量数据库、搜索/检索系统的工程经验，熟悉RAG架构设计与优化； 2. 熟悉LLM推理服务部署与优化（如vLLM、TGI、TensorRT-LLM等），有高并发AI服务的工程经验； 3. 有构建Agent评估环境的经验，包括仿真交互场景搭建、自动化评测框架设计、端到端benchmark构建等。

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