特斯拉Sr. AI DevOps Engineer - IT Support
任职要求
• Educational Background: Bachelor’s degree or above in relevant fields such as Computer Science, Artificial Intelligence, Software Engineering, or Electronic Engineering. • Work Experience: At least 3 years of experience in AI development, DevOps, or automated deployment; experience in user support or IT operations (especially in tech or service-oriented environments) is preferred. • Technical Competencies: o Proficiency in the Python programming language, capable of independently writing efficient AI algorithm code, data processing scripts, and DevOps automation scripts, with expertise in implementing FT, SFT, and RL algorithms. o Mastery of Kubernetes (K8s) and Docker technologies, with practical experience in containerized deployment, cluster management, and operations in production environments, supporting AI models tuned for real-time user interactions. o Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch), able to independently design, train, and optimize AI models for user support scenarios, including advanced tuning techniques and underlying technologies like neural networks, Transformer architectures, attention mechanisms, and model compression. o Familiarity with CI/CD processes and toolchains (e.g., Jenkins, GitLab CI), and ability to use tools like Terraform and Ansible to implement automated building, testing, and deployment of AI systems with integrated tuning pipelines. o Proficiency in …
工作职责
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.
Technical Development & Architecture * Design and implement scalable AI/ML solutions for Compliance use cases * Lead the development of efficient ML models and end‑to‑end data processing pipelines from ingestion to serving. * Build robust, production-grade AI services using Python and modern ML frameworks. * Make and document sound architectural decisions, ensuring systems are scalable, secure, and cost‑effective. * Establish and maintain high engineering standards, including testing, monitoring, and documentation. Engineering Leadership * Partner closely with data scientists, product managers, and operations teams to deliver end‑to‑end AI/ML solutions. * Define and evolve the technical architecture for AI-powered features and platforms. * Lead code reviews, enforce best practices, and elevate engineering quality across the team. * Continuously improve AI system performance, reliability, and latency through experimentation and optimization. Technical Collaboration & Operations * Work with cross‑functional partners to understand requirements, refine scope, and prioritize technical work. * Provide technical guidance and mentorship to junior and mid‑level engineers. * Collaborate with platform and DevOps teams to ensure smooth deployment, monitoring, and maintenance of AI systems. * Implement and evolve ML Ops practices (e.g., CI/CD for models, feature stores, model monitoring, and retraining workflows).
Every day will bring new and exciting challenges on the job while you: - Act as a strategic advisor for customers' Generative AI initiatives and internal AI agent innovation - Drive the development and implementation of collaborative AI agents within the TAM organization - Lead technical discussions around AWS AI services including Bedrock, Claude, and Amazon Q. - Make recommendations on AI architecture, security, cost optimization, and operational excellence - Champion internal AI agent success stories to inspire customer innovation - Complete analysis and present periodic reviews of AI workload performance - Guide customers in developing responsible AI practices while ensuring security and compliance - Foster an ecosystem where AI and humans progress together through knowledge sharing - Work with AWS AI/ML service teams to advocate for customer needs - Participate in customer requested meetings (onsite or via phone) - Work directly with Amazon Web Service engineers to ensure rapid resolution of AI-related issues - Available in non-business hours to handle urgent issues ------------------------------------------------
You will lead and support your team as a people manager by fostering empowerment and accountability, guided by the principles of model, coach and care: • You will lead teams in identifying and advancing new business opportunities, integrating impactful industry insights into customer engagements, and driving strategic projects and high-impact AI solution deployments that deliver measurable business value. • You will guide your team in developing and executing opportunity strategies through effective orchestration, ensuring alignment with customer needs. This includes coaching on how to engage customers to uncover business challenges and facilitate meaningful solution discussions. • You will coach your team on applying the orchestration model and support them in building a strong partner network to drive cross-sell and up-sell motions. • Leveraging your technical and market expertise, you will mentor your team on connecting Microsoft solutions to customer outcomes and act as a thought leader in AI transformation conversations. • You will define long-term customer satisfaction strategies, lead whitespace analysis, and participate in strategic territory planning. You’ll ensure alignment across departments through regular ROB reviews and planning sessions. • You will be accountable for achieving sales targets and maintaining operational excellence. This includes coaching your team on product and sales knowledge, ensuring completion of required training and certifications, and monitoring key performance metrics across the territory.
We empower our people to stay resilient and relevant in a constantly changing world. We're looking for people who are always searching for creative ways to grow and learn. People who want to make a real impact, now and in the future. Does that sound like you? Then it seems like you'd make a great addition to our vibrant international team. For our Siemens Advanta Consulting team, we are looking for AI Sr. Consultant to help us drive Advanta business within Siemens and beyond. We are a highly motivated team and excited to get to know you. You'll make an impact by 一、岗位概述 我们正在寻找一位具有 3-5 年人工智能或数据科学经验的工程师，帮助公司将 AI 技术深度融入生产制造全流程。您将与生产、质量、设备、IT/OT 团队协同，利用机器学习、深度学习与工业数据为制造现场创造切实可见的价值，提高产量、良率与设备稼动率，降低能耗与维护成本。 二、核心职责 1. 视觉质检 • 设计和部署基于 CNN/Transformer 的缺陷检测与分类模型，适配多品类、多光照、多批次的生产现场。 • 与质量工程师合作建立样本标注流程，持续提升模型召回率与精确率。 2. 预测性维护 • 采集并分析振动、声学、电流、电压等多模态传感器数据，构建剩余寿命预测（RUL）和故障预警模型。 • 将模型结果集成到 EAM/CMMS，实现从“计划检修”到“按状态检修”的转变。 3. 工艺与流程优化 • 运用时间序列分析、贝叶斯优化或强化学习，寻优关键工艺参数（温度、压力、速度等），提升良率、降低能耗和物料损耗。 • 与生产计划团队协作，开发动态排产与库存优化算法，缩短生产周期。 4. 数据工程 & MLOps • 搭建数据采集、清洗、标签管理及特征工程流程，保证数据质量和实时性。 • 负责模型在云端或边缘侧部署、监控与迭代，确保在工业环境下的稳定、低时延运行。 5. 跨部门协作 • 与 OT（PLC/SCADA/MES）及 IT 团队对接，实现从设备到模型再到业务系统的数据闭环。 • 将技术成果沉淀为规范、文档与最佳实践，提升公司 AI 工程化能力。