职位介绍
The MADE AI Engineer is primarily responsible for developing, deploying and optimizing AI solutions that improve manufacturing, quality, supply chain and office workflow efficiency. The role includes identifying AI application opportunities, building and validating AI models or AI-agent solutions, integrating AI tools into business and shopfloor processes, and working with cross-functional teams to accelerate intelligent transformation under MADE.

Besides overall AI engineering, this role will also support AI solution governance, user enablement, prompt and workflow design, data preparation and continuous improvement of deployed AI solutions. Work as an important MADE team representative to connect operations needs with practical AI implementation.

Essential Job Functions and Accountabilities:

Identify and evaluate AI use cases in manufacturing, quality, supply chain, engineering and office workflows.
- 在制造、质量、供应链、工程及办公工作流程中识别并评估AI应用场景；

Design, develop and deploy AI solutions such as computer vision inspection, OCR, predictive analytics, generative AI assistants and AI agents.
- 设计、开发并部署AI解决方案，如计算机视觉检测、OCR（光学字符识别）、预测分析、生成式AI助手及AI代理；

Work with stakeholders to translate business problems into AI solution requirements, data requirements and measurable outcomes.
- 与利益相关方合作，将业务问题转化为AI解决方案的需求、数据需求及可衡量的成果。

Support data collection, data cleaning, annotation, model evaluation and performance optimization activities.
支持数据收集、数据清洗、标注、模型评估及性能优化等工作。


Develop AI workflows, prompt strategies, agent logic and integration methods to improve task automation and decision support.
开发AI工作流程、提示策略、代理逻辑和集成方法，以提升任务自动化和决策支持能力。

Collaborate with digitalization and automation teams to integrate AI solutions into MES, ERP, vision systems, office tools and production workflows.
与数字化和自动化团队协作，将AI解决方案集成到MES、ERP、视觉系统、办公工具及生产流程中。

Conduct testing, validation and monitoring of deployed AI solutions to ensure reliability, accuracy and usability.
对已部署的AI解决方案进行测试、验证和监控，确保其可靠性、准确性和可用性。

Troubleshoot AI model, data or workflow issues and drive continuous improvement actions.
排查AI模型、数据或工作流程问题，并推动持续改进措施。

Provide user training, technical documentation and adoption support for deployed AI tools.
为已部署的AI工具提供用户培训、技术文档及采用支持。

Ensure AI solutions are aligned with business objectives, compliance requirements and data governance expectations.
确保AI解决方案与业务目标、合规要求及数据治理期望保持一致。

The ability to perform independently and as part of a team.

Must be able to influence and negotiate with people and resolve issues/problems.

Communicate recommendations and decisions across the organization.

Demonstrated ability to lead in a fast-paced technical environment, absorb technical information, review technical information and make technical decisions.

Other Job Requirements:

Occasional travel is required, including domestic and international travel.
需要偶尔出差，包括国内和国际出差。

Educational Qualifications:

Bachelor Degree in Computer Science, Artificial Intelligence, Data Science, Automation, Engineering or related field; Master degree is preferred.
计算机科学、人工智能、数据科学、自动化、工程或相关领域的学士学位；硕士学历优先。

Experience Qualifications:

5 years direct experience in AI engineering, data science, machine learning, computer vision or intelligent automation role.
5年AI工程、数据科学、机器学习、计算机视觉或智能自动化领域的直接工作经验。

Experience in biotech, pharmaceutical, manufacturing or industrial AI application is preferred.
有生物技术、制药、制造或工业AI应用经验者优先。

Direct experience in computer vision, OCR, machine learning model deployment, LLM application or AI-agent development is preferred.
有计算机视觉、OCR、机器学习模型部署、大语言模型（LLM）应用或AI代理开发经验者优先。

Familiarity with Python and common AI or ML frameworks and deployment workflows is preferred.

Experience with manufacturing, quality or supply chain business scenarios is highly preferred.

Proficient with Microsoft tools; Word, Excel, Power Point; experience with Python, SQL, Power BI or AI platforms is preferred.

Strong analytical, problem solving and teamwork skills are required.

Experience with structured problem solving and model performance analysis.

Ability to analyze and process data and draw the appropriate conclusions.

Demonstrated knowledge of AI lifecycle management, model validation and continuous improvement methodologies.

Experience prioritizing conflicting demands.

Excellent interpersonal, organizational, and influencing skills.

Experience working with cross-functional teams and facilitating teams to identify and implement solutions to complex problems.

Excellent oral and written communication skill in English, be able to work in a multicultural environment.

Practical experience in integrating AI tools with enterprise or manufacturing systems is preferred.

Ability to achieve goals independently within a challenging time frame.

Metrics:

Number of AI solutions deployed and adopted.

AI model or workflow accuracy and stability.

Productivity or HC saving enabled by AI.

Defect detection improvement or decision efficiency improvement.

User adoption and business impact of AI

苏州