阿里巴巴阿里国际站-AI+工程师-AI mode
任职要求
1. 本科及以上学历，计算机、人工智能、数学、统计或相关专业；3年以上软件开发经验，具备大模型工程化落地、微调及应用开发的实际项目经验。 2. 工程基础：精通Python，熟悉Java/Go/C++至少一种；熟练掌握主流工程框架（如 Spring Boot、Django、React/Vue 等），具备高并发、分布式系统设计与开发能力；熟悉数据库（MySQL/PostgreSQL）、缓存（Redis）、消息队列（Kafka/RabbitMQ）等中间件的选型、优化与运维。 3. ML与数据基础：熟练使用 Python 进行数据处理，掌握 Pandas、NumPy、SQL 等常用工具；理解常见机器学习与深度学习算法原理；熟练使用至少一种主流深度学习框架（如：PyTorch），并具备模型训练、评估与调优经验。 4. 大模型工程化：掌握大模型微调技术（如 SFT、LoRA、P-Tuning、RLHF），能完成模型适配与优化；深入理解并实践 RAG、Prompt Engine…
工作职责
1. 负责大模型在阿里国际站-AI mode场景中的架构设计与工程化落地，综合运用RAG、AI Agent、Function Calling、Prompt Engineering等技术，构建端到端AI应用，包括知识库构建、向量检索集成、AI工作流编排及与业务系统的深度对接。 2. 负责大模型的生命周期工程管理，基于百炼、ModelScope、Hugging Face 或 LLaMA-Factory 等平台，开展模型微调（SFT/LoRA/P-Tuning/RLHF）、部署、监控与持续迭代； 3. 设计并实现面向业务目标的自动化评测体系，结合人工评估与自动指标（如F1、BLEU、ROUGE及业务定制指标），驱动数据闭环与模型迭代。 4. 开发高可用、高并发的应用服务，通过优化推理API性能、缓存策略与系统架构，保障服务稳定性与可扩展性。 5. 能够跨职能协同产出，与产品、UI/UX、测试及运维等团队紧密协作，推动AI功能从原型验证到规模化上线的全链路交付。
1、负责AI应用的前端开发，构建交互性强、用户体验优秀的AI交互界面。 2、与AI团队紧密合作，将复杂的算法和模型以直观的方式呈现给用户。 3、参与前沿的Web技术研究，探索AI技术在前端领域的创新应用。 4、优化前端性能，确保AI应用在各种设备和浏览器上的稳定运行和快速响应。 5、编写可维护的代码，遵循最佳实践。 1、Responsible for front-end development of AI applications, building highly interactive and user-friendly AI interfaces. 2、Collaborate closely with AI teams to present complex algorithms and models in an intuitive manner to users. 3、Conduct research on cutting-edge web technologies and explore innovative applications of AI in the frontend domain. 4、Optimize frontend performance to ensure stable and responsive AI applications across various devices and browsers. 5、Write maintainable code following best practices and provide guidance and training on frontend development for the team.
1. 负责大模型在业务场景中的架构设计与工程化落地，综合运用RAG、AI Agent、Function Calling、Prompt Engineering等技术，构建端到端AI应用，包括知识库构建、向量检索集成、AI工作流编排及与业务系统的深度对接。 2. 负责大模型的生命周期工程管理，基于百炼、ModelScope、Hugging Face 或 LLaMA-Factory 等平台，开展模型微调（SFT/LoRA/P-Tuning/RLHF）、部署、监控与持续迭代； 3. 设计并实现面向业务目标的自动化评测体系，结合人工评估与自动指标（如F1、BLEU、ROUGE及业务定制指标），驱动数据闭环与模型迭代。 4. 开发高可用、高并发的应用服务，通过优化推理API性能、缓存策略与系统架构，保障服务稳定性与可扩展性。 5. 能够跨职能协同产出，与产品、UI/UX、测试及运维等团队紧密协作，推动AI功能从原型验证到规模化上线的全链路交付。
我们正在寻找有经验的 AI 基建开发工程师，负责构建企业级 AI 基础设施，包括 AIGW（AI Gateway / 大模型接入与运维）、智能 NPC 平台、MCP Hub 等关键 AI 基础设施的设计、开发和运维工作。您将参与大模型接入、能力编排、性能优化、评估体系建设以及成本管理等全链路工作，帮助业务更高效地使用大模型。 您将与 AI 应用、业务研发、平台团队紧密协作，构建稳定、高性能、可扩展的大模型服务体系。 主要职责： 1. 负责 AIGW（AI Gateway）的大模型接入、路由、限流、监控、运维体系建设与优化。 2. 实现并维护与主流大模型服务（OpenAI、Anthropic、Gemini、DeepSeek 等）的 API 集成体系。 3. 参与大模型能力评估：效果评测、推理性能优化、模型选型对比、成本分析与优化。 4. 构建并维护 LLM 应用相关的工具链，包括 Prompt 模板管理、RAG 访问层、评测框架。 5. 优化模型调用策略（缓存、多模型路由、降级策略、并发调度）。 6. 支持业务团队落地 AI 应用，协助完成模型选型、评估、接入、调试与规范化接入。 7. 编写并维护平台技术文档、接入文档、最佳实践与标准。 8. 持续跟进生成式 AI、大模型推理、Agent/MCP、模型加速等领域的最新进展并应用于平台。
The Role TESLA is offering a full-time IT Support DevOps AI position in the Information Technology Department (Work Location: Tesla Giga Factory Shanghai). If you are a versatile expert integrating AI development, DevOps practices—someone who can efficiently tackle challenges, solve complex technical problems in user support and experience scenarios, and reject repetitive and inefficient work patterns—this role is perfect for you. IT Support DevOps AI is a core role connecting the company’s IT systems and user-facing processes, standing at the forefront of enhanced user support implementation. You will engage in work across multiple domains, including AI technology R&D, containerized deployment, and operational support. Through technical practice, you will support the company in optimizing user interactions, improving support efficiency, and contributing to the core goal of user experience transformation. Responsibilities • Undertake AI algorithm R&D, model optimization, and training, with a strong emphasis on fine-tuning (FT), supervised fine-tuning (SFT), reinforcement learning (RL), and advanced tuning techniques; focus on user support scenarios such as data analysis, query resolution, issue detection, and automated assistance to ensure AI technology aligns with user experience needs. • Complete the deployment, monitoring, and scaling of AI solutions based on container technologies like Kubernetes (K8s) and Docker, ensuring high availability and stability of the system in the operational environment, while integrating AI underlying technologies like neural networks and Transformer architectures for efficient performance. • Participate in DevOps process development, optimize the full lifecycle of AI model and system development, testing, and deployment, and realize automated deployment, continuous integration (CI), and continuous delivery (CD), incorporating RL-based optimization and model tuning for adaptive user support systems. • Collaborate with user support-related departments such as helpdesk, customer service, and product teams to deeply understand user pain points and provide data-driven AI technical solutions, leveraging SFT and attention mechanisms to enhance personalized user experiences. • Respond quickly to technical requirements and faults in user-facing systems, troubleshoot issues in AI systems, container clusters, and network environments, minimize impacts on user interactions, and improve support efficiency and satisfaction through advanced AI tuning and underlying model diagnostics. • Track cutting-edge technologies in the AI and DevOps fields (e.g., large language models with FT/SFT/RL integration, cloud-native operations) and industry trends, promote the pre-research and application of new technologies in user support scenarios, and continuously optimize system performance using techniques like model compression and quantization.