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Open nowPosted 66 days ago

Agent架构工程师

dcar/referral673 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAgent架构工程师dcar/referral · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 66 days ago

The posting

职位概览：我们正在构建下一代 AI 驱动的汽车服务生态。作为 Agent 架构师，你将负责打造一套 One-Conversion 核心架构。通过 Core-Agent 调度中枢，横向赋能新车、二手车、导购、外呼等全业务场景，利用全局记忆（Memory）与长周期触达机制，实现从“流量接入”到“用户转化”的全链路智能化闭环。

岗位职责： 1、核心 Agent 调度架构建设： - 负责设计并实现 One-Conversion 统一 Agent 架构，以 Core-Agent 为分发逻辑核心； - 横向构建导购、网销、选买用、通用等垂直场景 Agent 能力； - 解决跨场景的分发与协同调度问题，确保 Agent 在复杂业务流中的逻辑一致性与任务接力； 2、全场景稳定性保障： - 融合二手车、新车、智新版等异构场景，建立标准化的 Agent 运行环境； - 实现全链路 Trace 追踪、性能打点、动态扩缩容及自动化降级方案； - 针对 LLM 波动性，设计健壮的容错机制与异常处理链路，确保线上服务的 SLA； 3、全局语义化 Memory 体系： - 打通全场景用户数据，构建动态车辆档案、用户画像及场景化短记忆； - 语义化存储： 建设多模态长短期记忆，实现非结构化信息的结构化沉淀； - 意图提纯： 研发遗忘机制、扩散去噪及重要性打分算法，提升记忆质量； - 高效检索： 攻克向量检索增强（RAG）、逻辑推理增强及多跳关联检索难题； - 进化反思： 建立 Agent 闭环学习与跨域记忆迁移能力，让系统具备“越用越聪明”的特征； 4、长周期智能触达： - 基于用户行为与意愿度实时建模，打造主动触达引擎； - 支持跨场景（导购/二手车/新车）的长周期精准介入，优化用户转化漏斗。

为什么加入我们？ - 场景真实： 拥有海量真实的二手车、新车交易场景数据，你的 Agent 将直接影响千万级交易决策； - 技术前沿： 避开简单的“套壳”开发，深耕 Memory 机制与跨场景分发等 Agentic Workflow 核心难题； - 资源支持： 提供充足的算力支持与跨团队协作机制。

1、技术功底： 扎实的计算机基础，丰富的后端研发经验。精通 Golang 或 Python 及其主流技术栈（如 Gin/Django/FastAPI）； 2、大模型实战： 了解LLM原理，熟悉常用的Agent框架经验者优先； 3、架构能力： 熟悉分布式架构、微服务治理、高并发处理； 对向量数据库（Milvus/Pinecone 等）及 RAG 架构有深度实践； 4、产品思维： 具备优秀的产品意识，能从技术视角反推业务创新，不局限于“按需求开发”； 5、软素质： 强烈的求知欲与好奇心，能敏锐捕捉业界（如 OpenAI, Anthropic, Open-source SOTA）的最新动态并快速转化； 6、加分项： 拥有复杂多轮对话系统、智能客服、或汽车行业垂直领域 Agent 开发经验者。

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