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),支持大模型微调与知识库构建。 4、熟悉 ClickHouse 等 OLAP 引擎的选型逻辑,具备大数据项目架构经验,可设计面向 AI 分析的存储与计算方案(如多源数据融合、查询性能优化)。 5、 有大型分布式微服务项目开发经验,尤其熟悉 BI、数据分析类产品的架构(如支持百万级数据量的智能查询),具备 AI 功能落地(如 NL2SQL 引擎、智能可视化)的实战经验者优先。 6、 具备强问题解决能力,能快速定位 AI 模块异常(如模型生成错误 SQL、语义解析偏差),并通过技术手段(如日志分析、模型调试)推动优化。 加分项 1.在同等条件下,通过腾讯云认证或取得同等资格认证的候选人,我们会优先考虑。
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.
Hadoop+
https://www.runoob.com/w3cnote/hadoop-tutorial.html
Hadoop 为庞大的计算机集群提供可靠的、可伸缩的应用层计算和存储支持,它允许使用简单的编程模型跨计算机群集分布式处理大型数据集,并且支持在单台计算机到几千台计算机之间进行扩展。
[英文] Hadoop Tutorial
https://www.tutorialspoint.com/hadoop/index.htm
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models.
Hive+
[英文] Hive Tutorial
https://www.tutorialspoint.com/hive/index.htm
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data, and makes querying and analyzing easy.
https://www.youtube.com/watch?v=D4HqQ8-Ja9Y
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
Flink+
https://nightlies.apache.org/flink/flink-docs-release-2.0/docs/learn-flink/overview/
This training presents an introduction to Apache Flink that includes just enough to get you started writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of (ultimately important) details.
https://www.youtube.com/watch?v=WajYe9iA2Uk&list=PLa7VYi0yPIH2GTo3vRtX8w9tgNTTyYSux
Today’s businesses are increasingly software-defined, and their business processes are being automated. Whether it’s orders and shipments, or downloads and clicks, business events can always be streamed. Flink can be used to manipulate, process, and react to these streaming events as they occur.
大数据+
https://www.youtube.com/watch?v=bAyrObl7TYE
https://www.youtube.com/watch?v=H4bf_uuMC-g
With all this talk of Big Data, we got Rebecca Tickle to explain just what makes data into Big Data.
特征工程+
https://www.ibm.com/think/topics/feature-engineering
Feature engineering preprocesses raw data into a machine-readable format. It optimizes ML model performance by transforming and selecting relevant features.
https://www.kaggle.com/learn/feature-engineering
Better features make better models. Discover how to get the most out of your data.
ClickHouse+
[英文] Advanced Tutorial
https://clickhouse.com/docs/tutorial
Learn how to ingest and query data in ClickHouse using the New York City taxi example dataset.
https://www.youtube.com/watch?v=FtoWGT7kS-c
ClickHouse is an open-source column-oriented DBMS for online analytical processing that allows users to generate analytical reports using SQL queries in real-time.
https://www.youtube.com/watch?v=Rhe-kUyrFUE&list=PL0Z2YDlm0b3gcY5R_MUo4fT5bPqUQ66ep
OLAP+
https://www.youtube.com/watch?v=iw-5kFzIdgY
OLAP (for online analytical processing) is software for performing multidimensional analysis at high speeds on large volumes of data from a data warehouse, data mart, or some other unified, centralized data store.
数据分析+
[英文] Data Analyst Roadmap
https://roadmap.sh/data-analyst
Step by step guide to becoming an Data Analyst in 2025
SQL+
https://liaoxuefeng.com/books/sql/introduction/index.html
什么是SQL?简单地说,SQL就是访问和处理关系数据库的计算机标准语言。
https://sqlbolt.com/
Learn SQL with simple, interactive exercises.
https://www.youtube.com/watch?v=p3qvj9hO_Bo
In this video we will cover everything you need to know about SQL in only 60 minutes.
相关职位
社招信息技术类
• 参与机器学习/深度学习算法(推荐、预测、分类等)的设计、实现与优化 • 数据清洗、特征工程与探索性分析,构建高质量训练/测试集 • 设计并执行实验(A/B Test、交叉验证等),分析结果并持续迭代 • 协助模型上线部署与监控,保障服务稳定性
更新于 2025-06-19
社招1年以上核心本地商业-业
1.主导业务大项目的方案设计和开发; 2.参与系统的高可用建设,做好系统日常运维,确保系统稳定; 3.参与设计并完成架构演进的实施; 4.发现并解决当前系统中存在的问题,持续提升系统效率和质量; 5.指导新人,积极输出实践经验,促进共同进步。
更新于 2025-06-16