影石高级AI开发工程师

参与公司AI应用项目的设计、开发与交付，负责核心功能模块的实现与优化。 深度参与基于大语言模型（LLM）的应用开发，包括RAG（检索增强生成）、Agent（智能体）等技术方案的落地。 负责AI项目中数据流、模型调用、检索框架等系统架构的搭建与性能优化。 跟进AI前沿技术，持续优化和升级现有AI应用，保障产品的稳定性与高效性。 跨团队协作，推动AI项目顺利交付，支持客户的定制化需求。

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.

1.Agent核心框架研发： 负责排障AI Agent的核心框架设计与开发，包括任务规划（Planning）、工具调用（Tool-use）、记忆（Memory）等关键模块，持续提升Agent的自主决策与执行能力； 2.Agent效果评测与迭代： 主导设计并落地Agent自动化评测体系，并基于线上失效案例（如规划错误、幻觉）的深入分析，驱动模型、Prompt及工具链的持续优化； 3.大模型后训练与优化： 负责大模型的后训练流程，包括构建高质量SFT数据集、实施Fine-tuning与RLHF/DPO等优化策略，并建立评测-训练-部署的闭环，持续提升模型在排障领域的专业能力。

1.Agent核心框架研发： 负责排障AI Agent的核心框架设计与开发，包括任务规划（Planning）、工具调用（Tool-use）、记忆（Memory）等关键模块，持续提升Agent的自主决策与执行能力； 2.Agent效果评测与迭代： 主导设计并落地Agent自动化评测体系，并基于线上失效案例（如规划错误、幻觉）的深入分析，驱动模型、Prompt及工具链的持续优化； 3.大模型后训练与优化： 负责大模型的后训练流程，包括构建高质量SFT数据集、实施Fine-tuning与RLHF/DPO等优化策略，并建立评测-训练-部署的闭环，持续提升模型在排障领域的专业能力。