JMP_ AI Operation Excellence Expert（VM）

Job Responsibilities / 工作职责

1. Business-Driven AI Strategy & Architecture业务驱动的AI战略与架构设计

Define AI and industrial digitalization strategies aligned with enterprise business objectives. Translate operational challenges and business opportunities into scalable AI solution roadmaps. Ensure technology investments directly support measurable business value realization. Build modular, reusable, and service-oriented AI platform capabilities. Design “Minimum Reusable Flexible Development Units” to accelerate rapid deployment and scalable replication across business scenarios.

制定与企业业务目标一致的AI及工业数字化战略。将运营挑战与业务机会转化为可落地的AI解决方案路线图。确保技术投入能够直接支撑业务价值实现。构建模块化、可复用、服务化的AI平台能力。设计“最小可复用柔性开发单元”，实现业务场景快速部署与规模化复制。

2. Industrial AI Transformation Leadership工业AI转型领导能力

Lead enterprise-level AI transformation initiatives across manufacturing and operations. Drive AI adoption by embedding intelligent capabilities into operational workflows and decision-making processes. Collaborate with business leaders to identify high-value industrial AI use cases.  Balance strategic architecture planning with practical execution and delivery.

主导制造与运营领域企业级AI转型项目。将智能能力嵌入业务流程与运营决策体系，推动AI真正落地。与业务管理层协同识别高价值AI应用场景。平衡战略规划与实际落地执行能力。

3. AI Platform & Data Ecosystem Engineering AI平台与数据生态建设

Architect enterprise AI platforms integrating data, analytics, AI models, workflows, and industrial systems. Enable cloud-edge-device collaborative architecture. Standardize reusable AI services, APIs, data models, and workflow components. Establish scalable data governance and industrial knowledge frameworks.

构建融合数据、分析、AI模型、工作流及工业系统的企业AI平台。建设云-边-端协同架构。标准化AI服务、API、数据模型及流程组件。建立可扩展的数据治理与工业知识体系。

4. AI Productization & Continuous Innovation AI产品化与持续创新

Drive AI capability productization for sustainable enterprise reuse. Promote agile and iterative AI solution delivery. Introduce Generative AI, AI Agents, and Industrial Copilot capabilities into industrial operations. Continuously optimize AI architecture for scalability, flexibility, and business agility.

推动AI能力产品化，实现企业级持续复用。推进敏捷化与迭代式AI交付模式。推动生成式AI、AI Agent及工业Copilot在业务中的应用。持续优化AI架构的扩展性、柔性与业务敏捷性。

5. Cross-Functional Collaboration & Executive Communication 跨部门协同与高层沟通

Act as the bridge between business stakeholders and technology teams. Communicate strategic AI vision and transformation roadmap to executives and customers. Support enterprise consulting, solution workshops, and strategic planning sessions.

作为业务与技术团队之间的桥梁。向管理层及客户清晰传达AI战略与转型路线。支持企业咨询、方案共创及战略规划工作。

Strategic & Business Capability / 战略与业务能力

Strong capability in aligning technology strategy with business value realization. Deep understanding of industrial operations, manufacturing processes, and enterprise transformation. Ability to identify scalable AI business opportunities and transformation priorities. Strong systems thinking and platform-oriented architecture mindset.

具备将技术战略与业务价值实现深度融合的能力。 深刻理解工业运营、制造流程及企业转型逻辑。 能识别可规模化推广的AI业务机会与转型优先级。 具备系统性思维与平台化架构能力。

Technical Capability / 技术能力

Well mastered and good understanding of symbolic AI, knowledge representation, inference engines, and hybrid AI approaches (symbolic + data-driven), Strong cability in AI innovation to industrialization collaborating with business doman experts and Product managers. Strong AI full-stack development capability. Proficiency in Python, SQL, APIs, microservices, and cloud-native architecture. Experience with industrial IoT, MES, SCADA, PLC, and data platforms. Good Understanding of Digital Twin, Industrial Metaverse, AI Agents, and Generative AI technologies. High learning capability of market latest AI technologies.

深入掌握并充分理解Symbolic AI、Knowledge Representation、Inference Engine以及混合式AI架构（符号推理 + 数据驱动）相关理论与实践。具备较强的AI创新产业化能力，能够与业务领域专家及产品经理高效协同，推动AI技术从创新概念到业务落地与规模化应用。具备扎实的AI全栈开发能力。精通 Python、SQL、API、Microservices及云原生架构。具备工业IoT、MES、SCADA、PLC及工业数据平台相关经验。对数字孪生Digital Twin、Industrial Metaverse、AI Agent以及生成式AI等技术具备良好的理解与项目经验认知。 对于市面最新的热门AI技术有超强的学习能力。

Experience 经验

• 2-3+ years of experience in AI research, applied AI, or advanced analytics, preferably with exposure to industrial or manufacturing-related scenarios
• Experience participating in AI solution design or feasibility studies, from problem definition to conceptual solution architecture
• Hands-on experience developing proof-of-concept (PoC) or prototype AI models rather than full production systems
• Experience working with cross-functional teams (e.g., product, IT, data teams) to translate business problems into AI-driven approaches
• Proven success in delivering enterprise-scale industrial AI projects. Experience designing reusable enterprise AI platforms or digital ecosystems. Global project leadership experience is highly preferred.
• 具备2–3年以上AI研究、应用型人工智能或高级数据分析相关经验，优先考虑具有工业或制造业场景经验者。
• 参与过AI解决方案设计或可行性研究工作，能够从问题定义阶段到概念性架构设计阶段进行完整方案构建。
• 具备动手开发AI概念验证（PoC）或原型模型的经验，而非仅限于生产级系统的工程实现。
• 具备跨职能团队协作经验（如产品、IT、数据团队等），能够将业务问题有效转化为AI驱动的解决路径与技术方案。
• 优先考虑具备大型企业级工业AI项目成功交付经验，并有企业级AI平台或数字化生态系统架构设计经验，具备全球化项目管理或跨区域项目领导经验的候选人。

Education 教育背景

Master’s degree or above in Computer Science, Automation, Information Systems, industry Engineering, Applied Mathematics, Statistics or a related field or related fields, strong ability to read and analyze technical and research literature. Formal training or coursework related to knowledge-based systems, decision systems, or industrial informatics is a plus。

计算机科学、自动化、信息系统、工业工程、应用数学、统计学或相关领域的硕士及以上学历。具备较强的技术与科研文献阅读与分析能力。具备知识型系统、决策系统或工业信息学相关的正式培训或课程学习经历者优先。