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\n2、参与LLM应用工程化能力建设，包括RAG、Agent-Workflow、工具调用、上下文管理、任务编排、结果评测等; \n3、将Agent能力与内部平台、监控告警、日志系统、知识库、发布系统、资源治理系统等进行集成，提升复杂问题处理效率; \n4、参与面向训练/推理场景的稳定性诊断能力建设，支持日志分析、告警归因、变更排查、故障辅助定位等场景; \n5、持续优化Agent在真实生产环境中的准确性、稳定性、可控性和用户体验; \n6、跟进LLM Agent、AIOps、MLOps、AI Infra等方向的技术进展，并推动前沿技术在团队业务场景中落地。\n任职要求 / Requirements\n1、2027届获得本科及以上学历，计算机、软件工程、人工智能、数据科学、自动化、电子信息等相关专业; \n2、具备扎实的计算机基础，熟悉操作系统、计算机网络、数据库、数据结构与算法等基础知识; \n3、熟悉至少一门编程语言，如Go、Python等，具备良好的代码能力和工程实现能力; \n4、了解LLM应用开发相关技术，如RAG、Prompt Engineering、Agent、Function Calling、Embedding、向量检索、知识库构建等; \n5、具备AI相关实习经历，包括但不限于LLM应用、智能问答、Agent平台、AIOps、MLOps、机器学习平台、数据分析平台、AI工具链等方向; \n6、具备良好的学习能力、问题拆解能力和沟通协作能力，能够在复杂业务场景中理解问题并推动工程落地。\n加分项\n1、有LLM Agent、RAG、智能问答、Copilot、AI助手、AIOps平台等项目或实习经验; \n2、熟悉LangChain、LlamaIndex、MCP、向量数据库、模型评测、上下文工程等相关技术; \n3、熟悉Kubernetes、云原生、微服务、可观测、日志分析、监控告警、CI/CD等工程平台技术; \n4、有后端平台开发、分布式系统、工程效能平台或基础设施平台相关实践经验; \n5、对AI Infra、AIOps、研发效能、稳定性治理等方向有持续兴趣。\n你将获得\n1、深入参与大规模AI训练与推理基础设施中的Agent平台建设; \n2、在真实生产场景中探索LLM Agent的工程化落地，解决复杂系统诊断和效率提升问题; \n3、接触AIOps、AI Infra、资源治理、可观测、稳定性、研发效能等平台方向; 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