携程资深AI后端开发工程师(MJ030170)
社招全职旅游业务开发地点:上海状态:招聘
任职要求
有算法平台开发或与算法团队深度合作经验,特别是模型部署、模型在线推理服务的实际项目经验的优先具备大语言模型或复杂模型在线部署(Triton, TorchServe, TensorFlow Serving)、性能调优和推理加速的实际项目经验的优先熟悉智能Agent系统核心模块(如知识库/RAG/记忆管理/工具调用)的设计或开发经验,熟悉相关框架(LangChain/Llama…
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工作职责
参与大规模语言模型(LLM)及其他AI模型的在线服务部署、性能调优与推理加速,确保高并发、低延迟、高可用的模型服务。参与设计和开发智能Agent运行框架,实现核心模块,包括不限于知识库存储与检索(RAG)、Agent的长短期记忆存储、对接多模型协作协议(如MCP/A2A)等新型交互机制。负责搜索推荐算法工程后端系统的设计、开发和维护,保障系统稳定高效运行,实现高并发在线特征工程、模型推理服务集成和服务质量监控等核心模块。参与系统架构的设计、优化及核心模块的代码实现(Java为主)。与产品经理、算法工程师、前端工程师紧密协作,理解需求,设计技术方案并推动落地。负责系统的日常维护、监控、故障排查和性能调优。
包括英文材料
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
性能调优+
https://goperf.dev/
The Go App Optimization Guide is a series of in-depth, technical articles for developers who want to get more performance out of their Go code without relying on guesswork or cargo cult patterns.
https://web.dev/learn/performance
This course is designed for those new to web performance, a vital aspect of the user experience.
https://www.ibm.com/think/insights/application-performance-optimization
Application performance is not just a simple concern for most organizations; it’s a critical factor in their business’s success.
https://www.oreilly.com/library/view/optimizing-java/9781492039259/
Performance tuning is an experimental science, but that doesn’t mean engineers should resort to guesswork and folklore to get the job done.
AI agent+
https://www.ibm.com/think/ai-agents
Your one-stop resource for gaining in-depth knowledge and hands-on applications of AI agents.
RAG+
https://www.youtube.com/watch?v=sVcwVQRHIc8
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer.
LangChain+
https://python.langchain.com/docs/tutorials/
New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications.
https://www.freecodecamp.org/news/beginners-guide-to-langchain/
LangChain is a popular framework for creating LLM-powered apps.
LlamaIndex+
https://developers.llamaindex.ai/python/framework/getting_started/starter_example/
This tutorial will show you how to get started building agents with LlamaIndex.
https://www.ibm.com/think/tutorials/llamaindex-rag
LlamaIndex is a powerful open source framework that simplifies the process of building RAG pipelines.
MCP+
https://www.youtube.com/watch?v=eur8dUO9mvE
Unlock the secrets of MCP! 🚀 Dive into the world of Model Context Protocol and learn how to seamlessly connect AI agents to databases, APIs, and more. Roy Derks breaks down its components, from hosts to servers, and showcases real-world applications. Gain the knowledge to revolutionize your AI projects!
https://www.youtube.com/watch?v=L94WBLL0KjY
Let's talk about MCP or the Model Context Protocol.
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