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AI 全栈工程师(AI Agent 方向)

AI LLM Technology Architecture Associate Manager | Mid-Level | Full time
Job No. 14477301 | Shanghai
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岗位职责

负责 AI 产品从 0 到 1 全流程落地,聚焦 AI Agent 与 RAG 系统的工程化开发,践行 “工程化驾驭大模型” 的 Harness Engineer 理念,支撑业务场景智能化升级。 主导 AI Agent 架构设计与开发:基于 LangGraph/LangChain/AgentScope/Google-ADK 实现工作流编排、工具链开发、多 Agent 协作及 Human-in-the-Loop 交互,完成复杂任务自动化与智能决策落地。 构建并优化端到端 RAG 系统:负责文档解析、分块、向量化、多路检索、重排序全流程设计,结合上下文工程(Query Rewrite、动态 Prompt、对话历史管理)提升大模型推理质量。 实现大模型工程化落地:完成多模型集成调度、流式输出、提示词工程优化,将 AI 能力与业务场景深度融合。 搭建高可用后端服务:基于 Python(FastAPI/Flask)或 Go(Gin)开发 API 与业务逻辑层,精通异步编程与微服务架构,保障系统稳定性与性能。 负责项目部署运维与架构优化:基于 Docker/K8s 完成容器化部署,搭建 CI/CD 流程,保障项目高效迭代与稳定运行。 对接业务团队,拆解需求并转化为技术方案,参与技术选型与架构优化,沉淀最佳实践并推动团队技术能力提升。

任职要求

本科及以上学历,计算机 / 人工智能等相关专业,3 年及以上 AI 相关或全栈开发经验,有 AI 产品落地经验优先。 精通 AI Agent 工程化开发:熟练使用 LangGraph/LangChain/AgentScope/Google-ADK,具备工作流编排、工具链开发、多 Agent 协作及对话状态管理的落地经验。 具备扎实的 RAG 与上下文工程能力:熟悉文档解析、向量数据库(Milvus/FAISS 等)、多路检索、Reranker 优化,掌握 Query Rewrite、动态 Prompt、对话历史压缩等优化手段。 熟练掌握大模型工程化技术:提示词工程、多模型调度(Azure OpenAI / 通义千问等)、流式输出,了解模型微调优先。 后端开发能力:精通 Python(FastAPI 优先),熟悉异步编程、微服务架构;了解 Spring AI/Spring Boot 者优先。 熟悉前端开发基础(Vue/React)、容器化部署(Docker/K8s)及 CI/CD 流程,能独立完成项目全流程交付。 具备良好的需求拆解与问题排查能力,关注 AI 前沿技术,能推动业务场景的技术创新与优化。

Shanghai

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