工作职责：
1. 与制造、工程及 IT 团队合作，识别产线场景中的 AI/ML 应用机会（如预测性维护、质量检测、能耗优化、产线调度等），并将其转化为可落地的技术方案。
2. 负责制造自动化与数据应用的设计、开发与测试，覆盖数据采集、特征工程、模型训练、部署与可视化等完整流程。
3. 运用机器学习、深度学习、计算机视觉及时序分析技术，提升设备预测性维护能力、质量检测准确度、良率分析效率及关键 KPI 表现。
4. 构建数据管道、数据平台与 MLOps 流程，确保模型在生产环境中稳定运行、可监控与可迭代。
5. 跟踪前沿 AI 技术趋势，参与创新方案的快速原型开发与验证，并推动优秀技术在工厂规模化落地。
6. 撰写技术文档、开发指南与实践沉淀材料，支持组织技术能力持续提升。
职位要求：
1. 计算机、软件工程、自动化、电子、工业工程、人工智能等相关专业，2025/2026 届本科毕业生。
2. 至少掌握一种编程语言（Python/C#/C++/Java），熟悉常见 AI 框架（如 TensorFlow、PyTorch、Scikit-learn 等）。
3. 了解基本的机器学习方法，有项目、竞赛或科研经历者优先。
4. 熟悉 SQL，了解常见数据平台（Hadoop/Spark/Flink）者优先。
5. 了解 MES、SCADA、PLC、工业通信协议（如 OPC UA、MQTT）等工业自动化基础者更佳。
6. 具备良好的跨团队沟通能力，主动负责，愿意持续学习。
7. 英语读写流利，能够支持英文技术交流。
ESSENTIAL DUTIES AND RESPONSIBILITIES:
1. Partner with manufacturing, engineering and IT teams to identify AI/ML opportunities on the shop floor，such as predictive maintenance, quality inspection, yield analysis, energy optimization and production scheduling and translate them into scalable technical solutions.
2. Design, develop, test and deploy data & automation applications, including data ingestion, feature engineering, model training, inference deployment and data visualization.
3. Apply machine learning, deep learning, computer vision and time-series analytics to improve equipment reliability, inspection accuracy, yield performance and other key manufacturing KPIs.
4. Build data pipelines, data platforms and MLOps workflows to ensure stable, traceable and continuously improving model operations in production environments.
5. Track cutting-edge AI technologies, develop rapid prototypes and accelerate the scale-up of innovative solutions across the factory.
6. Create technical documentation, guidelines and best-practice playbooks to support organizational learning and capability growth.
QUALIFICATIONS REQUIRED:
1. Bachelor’s degree graduates in 2025 or 2026 in Computer Science, Software Engineering, Automation, Electronics, Industrial Engineering, AI or other related fields.
2. Solid programming skills in at least one of the following (Python/C#/C++/Java); familiarity with common AI frameworks such as TensorFlow, PyTorch and Scikit-learn.
3. Basic understanding of classical machine-learning algorithms; hands-on project, competition or research experience is preferred.
4. Knowledge of SQL and familiarity with big-data platforms (Hadoop/Spark/Flink) is preferred.
5. Basic understanding of industrial automation, MES, SCADA, PLCs and industrial communication protocols (OPC UA, MQTT) is preferred.
6. Strong cross-functional communication and teamwork skills; proactive, self-driven and eager to learn continuously.
7. Fluent English (written & spoken) for technical communication.