TI-AI平台底座开发工程师-Golang/K8S
Tencent
TI-AI平台底座开发工程师-Golang/K8S
Job Description
TI-AI平台底座开发工程师-Golang/K8S
📋 Job Overview
The TI-AI Platform Base Development Engineer role at Tencent focuses on designing, developing, and optimizing the backend systems for Tencent Cloud's AI platform to support massive AI model training and inference scenarios. Responsibilities include building Kubernetes-based container orchestration frameworks, optimizing resource scheduling for heterogeneous computing like GPUs, and developing core components for task orchestration, monitoring, and automated operations. The position involves collaboration with algorithm and product teams to enhance platform performance, explore cutting-edge AI technologies, and drive industry ecosystem integration in sectors like finance, gaming, and entertainment.
📍 Location: Xi'an, China
🏢 Business Unit: CSIG
📄 Full Description
1.负责腾讯云AI平台后台系统的架构设计、开发与优化，支撑海量AI模型训练与推理场景，保障高并发、高可用、灵活扩缩容的云原生服务能力；
2.基于Kubernetes构建容器化调度框架，优化AI任务资源调度策略，提升GPU等异构算力利用率，支撑万亿级参数大模型的分布式训练需求；
3.开发AI平台核心组件，包括任务编排、监控告警、日志分析等模块，推动平台自动化与智能化运维能力建设；
4.结合腾讯云星脉高性能网络、向量数据库等底层技术，优化AI任务的全链路性能，降低训练与推理延迟；
5.参与AI大模型（如混元大模型）的工程化落地，提供分布式训练框架支持，解决模型部署中的性能瓶颈与资源管理问题1；
6.探索AI与云原生技术的结合，推动模型量化、动态批处理（Continuous Batching）等前沿技术的应用；
7.与算法团队、产品团队紧密协作，输出标准化API与开发者工具，提升平台易用性；
8.参与行业生态共建，推动AI平台在金融、游戏、泛娱乐等场景的规模化落地。
2.基于Kubernetes构建容器化调度框架，优化AI任务资源调度策略，提升GPU等异构算力利用率，支撑万亿级参数大模型的分布式训练需求；
3.开发AI平台核心组件，包括任务编排、监控告警、日志分析等模块，推动平台自动化与智能化运维能力建设；
4.结合腾讯云星脉高性能网络、向量数据库等底层技术，优化AI任务的全链路性能，降低训练与推理延迟；
5.参与AI大模型（如混元大模型）的工程化落地，提供分布式训练框架支持，解决模型部署中的性能瓶颈与资源管理问题1；
6.探索AI与云原生技术的结合，推动模型量化、动态批处理（Continuous Batching）等前沿技术的应用；
7.与算法团队、产品团队紧密协作，输出标准化API与开发者工具，提升平台易用性；
8.参与行业生态共建，推动AI平台在金融、游戏、泛娱乐等场景的规模化落地。
🎯 Key Responsibilities
- Responsible for the architecture design, development, and optimization of Tencent Cloud AI platform backend systems, supporting massive AI model training and inference scenarios, ensuring high concurrency, high availability, and flexible scalability of cloud-native service capabilities.
- Build containerized scheduling frameworks based on Kubernetes, optimize AI task resource scheduling strategies, improve utilization of heterogeneous computing power such as GPUs, and support distributed training needs for trillion-parameter large models.
- Develop core components of the AI platform, including task orchestration, monitoring and alerting, log analysis, and other modules, to promote the construction of automated and intelligent operation and maintenance capabilities for the platform.
- Combine underlying technologies such as Tencent Cloud's Xingmai high-performance network and vector databases to optimize the full-link performance of AI tasks and reduce training and inference latency.
- Participate in the engineering implementation of AI large models (such as the Hunyuan large model), provide distributed training framework support, and solve performance bottlenecks and resource management issues in model deployment.
- Explore the integration of AI and cloud-native technologies, and promote the application of cutting-edge technologies such as model quantization and dynamic batch processing (Continuous Batching).
- Closely collaborate with algorithm teams and product teams to output standardized APIs and developer tools, enhancing platform usability.
- Participate in industry ecosystem co-construction, promoting the large-scale implementation of the AI platform in scenarios such as finance, gaming, and general entertainment.
🛠️ Required Skills
- Golang programming
- Kubernetes expertise
- AI platform backend development
- Container orchestration and resource scheduling
- Distributed training frameworks for large models
- Performance optimization for AI tasks
- Collaboration with cross-functional teams
- Knowledge of cloud-native technologies and high-performance networks
Locations
- Xi'an, China
Salary
Estimated Salary Rangemedium confidence
250,000 - 450,000 CNY / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Golang programmingintermediate
- Kubernetes expertiseintermediate
- AI platform backend developmentintermediate
- Container orchestration and resource schedulingintermediate
- Distributed training frameworks for large modelsintermediate
- Performance optimization for AI tasksintermediate
- Collaboration with cross-functional teamsintermediate
- Knowledge of cloud-native technologies and high-performance networksintermediate
Responsibilities
- Responsible for the architecture design, development, and optimization of Tencent Cloud AI platform backend systems, supporting massive AI model training and inference scenarios, ensuring high concurrency, high availability, and flexible scalability of cloud-native service capabilities.
- Build containerized scheduling frameworks based on Kubernetes, optimize AI task resource scheduling strategies, improve utilization of heterogeneous computing power such as GPUs, and support distributed training needs for trillion-parameter large models.
- Develop core components of the AI platform, including task orchestration, monitoring and alerting, log analysis, and other modules, to promote the construction of automated and intelligent operation and maintenance capabilities for the platform.
- Combine underlying technologies such as Tencent Cloud's Xingmai high-performance network and vector databases to optimize the full-link performance of AI tasks and reduce training and inference latency.
- Participate in the engineering implementation of AI large models (such as the Hunyuan large model), provide distributed training framework support, and solve performance bottlenecks and resource management issues in model deployment.
- Explore the integration of AI and cloud-native technologies, and promote the application of cutting-edge technologies such as model quantization and dynamic batch processing (Continuous Batching).
- Closely collaborate with algorithm teams and product teams to output standardized APIs and developer tools, enhancing platform usability.
- Participate in industry ecosystem co-construction, promoting the large-scale implementation of the AI platform in scenarios such as finance, gaming, and general entertainment.
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Answer 10 quick questions to check your fit for TI-AI平台底座开发工程师-Golang/K8S @ Tencent.
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TI-AI平台底座开发工程师-Golang/K8S
Tencent
TI-AI平台底座开发工程师-Golang/K8S
Job Description
TI-AI平台底座开发工程师-Golang/K8S
📋 Job Overview
The TI-AI Platform Base Development Engineer role at Tencent focuses on designing, developing, and optimizing the backend systems for Tencent Cloud's AI platform to support massive AI model training and inference scenarios. Responsibilities include building Kubernetes-based container orchestration frameworks, optimizing resource scheduling for heterogeneous computing like GPUs, and developing core components for task orchestration, monitoring, and automated operations. The position involves collaboration with algorithm and product teams to enhance platform performance, explore cutting-edge AI technologies, and drive industry ecosystem integration in sectors like finance, gaming, and entertainment.
📍 Location: Xi'an, China
🏢 Business Unit: CSIG
📄 Full Description
1.负责腾讯云AI平台后台系统的架构设计、开发与优化，支撑海量AI模型训练与推理场景，保障高并发、高可用、灵活扩缩容的云原生服务能力；
2.基于Kubernetes构建容器化调度框架，优化AI任务资源调度策略，提升GPU等异构算力利用率，支撑万亿级参数大模型的分布式训练需求；
3.开发AI平台核心组件，包括任务编排、监控告警、日志分析等模块，推动平台自动化与智能化运维能力建设；
4.结合腾讯云星脉高性能网络、向量数据库等底层技术，优化AI任务的全链路性能，降低训练与推理延迟；
5.参与AI大模型（如混元大模型）的工程化落地，提供分布式训练框架支持，解决模型部署中的性能瓶颈与资源管理问题1；
6.探索AI与云原生技术的结合，推动模型量化、动态批处理（Continuous Batching）等前沿技术的应用；
7.与算法团队、产品团队紧密协作，输出标准化API与开发者工具，提升平台易用性；
8.参与行业生态共建，推动AI平台在金融、游戏、泛娱乐等场景的规模化落地。
2.基于Kubernetes构建容器化调度框架，优化AI任务资源调度策略，提升GPU等异构算力利用率，支撑万亿级参数大模型的分布式训练需求；
3.开发AI平台核心组件，包括任务编排、监控告警、日志分析等模块，推动平台自动化与智能化运维能力建设；
4.结合腾讯云星脉高性能网络、向量数据库等底层技术，优化AI任务的全链路性能，降低训练与推理延迟；
5.参与AI大模型（如混元大模型）的工程化落地，提供分布式训练框架支持，解决模型部署中的性能瓶颈与资源管理问题1；
6.探索AI与云原生技术的结合，推动模型量化、动态批处理（Continuous Batching）等前沿技术的应用；
7.与算法团队、产品团队紧密协作，输出标准化API与开发者工具，提升平台易用性；
8.参与行业生态共建，推动AI平台在金融、游戏、泛娱乐等场景的规模化落地。
🎯 Key Responsibilities
- Responsible for the architecture design, development, and optimization of Tencent Cloud AI platform backend systems, supporting massive AI model training and inference scenarios, ensuring high concurrency, high availability, and flexible scalability of cloud-native service capabilities.
- Build containerized scheduling frameworks based on Kubernetes, optimize AI task resource scheduling strategies, improve utilization of heterogeneous computing power such as GPUs, and support distributed training needs for trillion-parameter large models.
- Develop core components of the AI platform, including task orchestration, monitoring and alerting, log analysis, and other modules, to promote the construction of automated and intelligent operation and maintenance capabilities for the platform.
- Combine underlying technologies such as Tencent Cloud's Xingmai high-performance network and vector databases to optimize the full-link performance of AI tasks and reduce training and inference latency.
- Participate in the engineering implementation of AI large models (such as the Hunyuan large model), provide distributed training framework support, and solve performance bottlenecks and resource management issues in model deployment.
- Explore the integration of AI and cloud-native technologies, and promote the application of cutting-edge technologies such as model quantization and dynamic batch processing (Continuous Batching).
- Closely collaborate with algorithm teams and product teams to output standardized APIs and developer tools, enhancing platform usability.
- Participate in industry ecosystem co-construction, promoting the large-scale implementation of the AI platform in scenarios such as finance, gaming, and general entertainment.
🛠️ Required Skills
- Golang programming
- Kubernetes expertise
- AI platform backend development
- Container orchestration and resource scheduling
- Distributed training frameworks for large models
- Performance optimization for AI tasks
- Collaboration with cross-functional teams
- Knowledge of cloud-native technologies and high-performance networks
Locations
- Xi'an, China
Salary
Estimated Salary Rangemedium confidence
250,000 - 450,000 CNY / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Golang programmingintermediate
- Kubernetes expertiseintermediate
- AI platform backend developmentintermediate
- Container orchestration and resource schedulingintermediate
- Distributed training frameworks for large modelsintermediate
- Performance optimization for AI tasksintermediate
- Collaboration with cross-functional teamsintermediate
- Knowledge of cloud-native technologies and high-performance networksintermediate
Responsibilities
- Responsible for the architecture design, development, and optimization of Tencent Cloud AI platform backend systems, supporting massive AI model training and inference scenarios, ensuring high concurrency, high availability, and flexible scalability of cloud-native service capabilities.
- Build containerized scheduling frameworks based on Kubernetes, optimize AI task resource scheduling strategies, improve utilization of heterogeneous computing power such as GPUs, and support distributed training needs for trillion-parameter large models.
- Develop core components of the AI platform, including task orchestration, monitoring and alerting, log analysis, and other modules, to promote the construction of automated and intelligent operation and maintenance capabilities for the platform.
- Combine underlying technologies such as Tencent Cloud's Xingmai high-performance network and vector databases to optimize the full-link performance of AI tasks and reduce training and inference latency.
- Participate in the engineering implementation of AI large models (such as the Hunyuan large model), provide distributed training framework support, and solve performance bottlenecks and resource management issues in model deployment.
- Explore the integration of AI and cloud-native technologies, and promote the application of cutting-edge technologies such as model quantization and dynamic batch processing (Continuous Batching).
- Closely collaborate with algorithm teams and product teams to output standardized APIs and developer tools, enhancing platform usability.
- Participate in industry ecosystem co-construction, promoting the large-scale implementation of the AI platform in scenarios such as finance, gaming, and general entertainment.
Tags & Categories
Answer 10 quick questions to check your fit for TI-AI平台底座开发工程师-Golang/K8S @ Tencent.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs