腾讯腾讯云BI-后台开发工程师
社招全职3年以上CSIG技术地点:深圳状态:招聘
任职要求
1、具备扎实的编程基础,精通 Java/python任一开发语言,熟练运用 Spring Boot/Spring Cloud 等微服务框架,能高效实现 AI 模块与业务系统的对接(如大模型 API 集成、向量数据库交互) 2、 具备扎实的数据结构与算法能力,擅长设计高效的 AI 推理优化方案(如 Query 改写、模型轻量化),能通过代码优化提升 NL2SQL、RAG 等模块的响应速度(如首 token 耗时控制在 3s 内)。 3、 精通 Hadoop/Hive/Spark/Flink 等大数据套件的原理与实战,能搭建 AI 训练与推理所需的数据管道(如实时数据接入、特征工程),支持大模…
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工作职责
1.智能分析平台研发:主导腾讯云ChatBI 的架构设计,深度融合大模型技术(如 RAG、NL2SQL、NL2DSL),实现自然语言驱动的数据查询与可视化分析能力,推动产品向 AI 原生方向升级。 2.AI 驱动产品设计:负责腾讯云数据分析类产品的智能化迭代,基于 LLM 能力重构交互逻辑(如自然语言语义解析、动态知识注入),打造 “零代码” AI 分析体验,覆盖 SaaS 与私有化部署场景。 3.AI 技术方案落地:根据业务需求输出兼具创新性与可行性的技术方案,主导大模型微调(如领域适配、参数高效优化)、向量数据库集成、智能查询优化等核心模块开发,确保代码质量与工程落地性。 4.智能场景问题攻坚:针对 SaaS 与私有化客户的复杂需求,通过 AI 技术手段(如模型推理优化、实时数据处理)解决智能分析链路中的性能瓶颈、语义歧义等问题,保障 AI 功能在不同部署环境下的稳定性与准确性。
包括英文材料
Java+
https://www.youtube.com/watch?v=eIrMbAQSU34
Master Java – a must-have language for software development, Android apps, and more! ☕️ This beginner-friendly course takes you from basics to real coding skills.
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文,免费,零起点,完整示例,基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
Spring Boot+
https://spring.io/guides/gs/spring-boot
his guide provides a sampling of how Spring Boot helps you accelerate application development.
https://www.youtube.com/watch?v=Nv2DERaMx-4&list=PLzUMQwCOrQTksiYqoumAQxuhPNa3HqasL
The author teaches you how to use Spring Boot from a complete beginner, to building a REST API with a real database, Dockerising it and deploying it to the cloud.
Spring Cloud+
[英文] Spring Cloud Series
https://www.baeldung.com/spring-cloud-series
Learn Spring Cloud including concepts, additional libraries and examples for distributed systems.
微服务+
https://learn.microsoft.com/en-us/training/modules/dotnet-microservices/
Microservice applications are composed of small, independently versioned, and scalable customer-focused services that communicate with each other by using standard protocols and well-defined interfaces.
https://microservices.io/
Microservices - also known as the microservice architecture - is an architectural style that structures an application as a collection of two or more services.
https://spring.io/microservices
Building small, self-contained, ready to run applications can bring great flexibility and added resilience to your code.
https://www.ibm.com/think/topics/microservices
Microservices, or microservices architecture, is a cloud-native architectural approach in which a single application is composed of many loosely coupled and independently deployable smaller components or services.
https://www.youtube.com/watch?v=CqCDOosvZIk
https://www.youtube.com/watch?v=hmkF77F9TLw
Learn about software system design and microservices.
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
数据结构+
https://www.youtube.com/watch?v=8hly31xKli0
In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science.
https://www.youtube.com/watch?v=B31LgI4Y4DQ
Learn about data structures in this comprehensive course. We will be implementing these data structures in C or C++.
https://www.youtube.com/watch?v=CBYHwZcbD-s
Data Structures and Algorithms full course tutorial java
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/
RAG+
https://www.youtube.com/watch?v=sVcwVQRHIc8
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer.
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