AI Expert_PS
你的任务
Job Summary
As an AI Expert in RBCD/TEF3, you will be the technical driver of equipment-centric enterprise AI in BBM-CN — designing, deploying, and applying AI where it creates measurable value: on and around our manufacturing equipment. You will work deep inside the factory, side-by-side with equipment, maintenance, and operations teams, identifying real problems at the machine and solving them end-to-end with AI.
You own your deployment strategy: prioritising use cases by business value and feasibility — from predictive maintenance and anomaly detection to machine-integrated vision and LLM-based shop-floor assistance — always on a rock-solid technical foundation in machine learning, industrial data, and edge deployment, and aligned with Bosch enterprise AI platforms (e.g., M.AI.Co) and the plant’s IIoT and edge infrastructure. For you, AI is never the goal: every project starts from a real problem on the shop floor and ends with a measured, sustainable improvement in daily production.
Roles and Responsibilities
Equipment-Centric AI Strategy & Deployment
- Define and continuously refine the TEF3 AI strategy and roadmap for equipment and manufacturing technology — prioritising use cases by business value, data feasibility, and scalability, aligned with plant and PS/CE direction.
- Translate equipment pain points (downtime, scrap, cycle time, energy, maintenance cost) into clearly scoped AI use cases with quantified targets and business cases.
- Drive end-to-end deployment: from single-line pilot to plant-wide rollout and multi-plant replication, with defined acceptance criteria, KPI tracking, and sustainable handover to operations and maintenance.
- Cooperate with PS central departments and other plants to deploy standard AI solutions for equipment and to feed proven TEF3 solutions back into the standard portfolio.
AI Application Development on the Shop Floor
- Develop and deploy AI applications that solve concrete equipment problems: predictive maintenance (failure prediction, remaining useful life), anomaly detection on machine and process signals, process parameter optimisation, OEE improvement, and maintenance / spare-parts planning support.
- Develop computer-vision solutions for equipment-integrated quality and condition monitoring (e.g., on-machine inspection, AOI support).
- Build LLM-based applications that genuinely help the shop floor: retrieval-augmented assistants over equipment manuals, fault codes, and work instructions — applied where they clearly beat simpler solutions.
- Work directly at the machine with maintenance and equipment teams: observe, measure, and validate that solutions address root causes rather than symptoms.
Industrial Data & Deployment Foundation
- Design and implement industrial data pipelines: machine data acquisition (PLC, CNC, sensors via OPC UA / MQTT / fieldbus), edge processing, and historian / IIoT platform integration, ensuring data quality and governance at the source.
- Deploy AI on industrial edge devices and on-prem infrastructure: model optimisation (quantisation, ONNX, TensorRT), containerised serving, and robust integration into equipment environments.
- Establish MLOps practices for equipment AI: monitoring, drift detection, retraining, and full lifecycle management in production.
- Ensure IT/OT security and functional-safety requirements are respected in every deployment on and around equipment.
Leadership, Scaling & Knowledge Sharing
- Lead or accompany cross-functional project teams (equipment, maintenance, production, quality, IT) with full responsibility for costs, quality, and on-time delivery.
- Coach equipment and maintenance engineers in data and AI literacy; build pragmatic, lasting AI competence within TEF3.
- Monitor AI technology developments (foundation models, agentic frameworks, edge AI) and evaluate them soberly against real TEF3 use cases — adopting what solves problems, not what is fashionable.
Authority (mandatory for Grade 7 and above, optional for others)
- Define the AI deployment strategy and roadmap for equipment-centric AI within RBCD/TEF3 scope, with full responsibility for results.
- Lead AI projects and work packages cross-functionally; select technical approaches and tools within approved budgets.
- Cooperate with PS central departments to deploy and share standard solutions across plants.
- Coach and guide data enthusiasts and team members, supporting their competency build-up.
您的个人资料 Qualifications Requirement
- Master’s degree (or above) in Computer Science, AI/ML, Electrical / Automation Engineering, Mechanical Engineering, or a closely related field; exceptional Bachelor candidates with a strong delivery record will also be considered.
- 3+ years of hands-on experience in real AI/ML projects, including responsibility for deploying AI solutions in an industrial or manufacturing environment (equipment, maintenance, or production-technology domain is a strong plus).
- Proven track record of solving real problems: AI solutions delivered from prototype to serial production with measurable business impact (uptime, scrap, cost, energy).
- Strong proactive, self-driven learning mindset with a customer-oriented and CIP (Continuous Improvement) attitude; willingness to spend substantial time on the shop floor.
Job Relevant Knowledge and Experience
AI & Machine Learning Engineering
- Expert in Python; proficient in PyTorch and/or TensorFlow/Keras and scikit-learn.
- Strong in time-series machine learning on machine / process data: forecasting, anomaly detection, failure prediction; solid applied-statistics and signal-processing fundamentals.
- Proficient in computer vision: OpenCV (filtering, feature extraction, camera calibration) and detection / segmentation models (YOLO, Faster-RCNN, SAM) applied to industrial inspection tasks.
- Hands-on LLM application engineering: RAG (vector databases, embedding pipelines, context management), prompt engineering, tool-calling; awareness of agentic patterns and frameworks (LangGraph, MCP) — applied selectively where they add real value.
- Familiar with data labelling toolchains and data versioning (Label Studio, CVAT, DVC, MLflow).
Equipment & Industrial Domain Knowledge
- Understanding of manufacturing equipment and automation: PLC/CNC, robotics, drives, sensors, and industrial communication (OPC UA, MQTT, Profinet/EtherCAT).
- Familiar with equipment and maintenance processes: TPM, condition monitoring, spare-parts management, OEE, MTBF/MTTR; able to turn failure modes and maintenance records into data problems.
- Experience with industrial data infrastructure: historians, IIoT platforms, and edge computing in production environments.
- Structured problem-solving methods (8D, DMAIC, PDCA); Six Sigma certification is a plus.
Deployment, MLOps & Edge Engineering
- Proficient in Git and CI/CD pipelines for ML/AI systems; containerisation (Docker, Kubernetes) for model serving.
- Model optimisation for edge deployment: quantisation, ONNX, TensorRT.
- MLOps practices: experiment tracking (MLflow, W&B), model registry, monitoring, and drift management.
- Familiar with cloud / on-prem ML platforms (Azure ML, AWS SageMaker, or equivalent); IT/OT security awareness.
Domain & Soft Skills
- Ability to go deep into the factory: observe real processes, talk to operators and maintainers, and translate ambiguous shop-floor pain into well-scoped, valuable AI solutions.
- Owns a strategy and defends it with evidence — including the discipline to say “no” to AI use cases without business value.
- Strong analytical and structured problem-solving skills; excellent communication with both shop-floor and management stakeholders.
- Intercultural collaboration experience; comfortable working in cross-functional, international teams.
- Mandarin (working proficiency) and English (professional proficiency) required.
Competency Related Requirements
(mark 3 most important characteristics of each sub-group — marked in bold)
Entrepreneurial Mindset
Goal/Profit Orientation Cost Awareness Consistency Efficient Implementation Resource Management Process Control Strategic Thinking/Action Market/Customer Orientation Innovation Initiative Willingness to Take Risks Change Management
Social Competence
Cooperation Ability to Create and Use Networks Openness Credibility Reliability Share Knowledge and Information Ability to Work in Teams Oral/Written Communications Presentation Skills Manage Group Processes Negotiation Skills Intercultural Skills Handling Conflicts Foreign Language Ability Integrity
Technical and Methodological Competence
Overview Cross-functional/International Experience Interdisciplinary Way of Thinking Flexibility Project Management Analytical/Intellectual Skills Specialist Knowledge Recognized Expertise Creativity Continuous Learning
Leadership (required for all supervisory positions, optional for project leader positions)
Ambition to Lead Conviction Strength of Character Energy Courage Optimism Stability Charisma Integrity Decision-making Strength Ability to Motivate/Animate Others Policy Deployment/Development of targets Delegation Assess Performance Give Feedback Develop Staff Recognize and Encourage Talent Mentoring/Coaching
Key Performance Indicators
- OTD (On-Time Delivery) of AI projects according to RBCD/TEF3 business requirements.
- Measurable business impact of deployed AI solutions: machine uptime / OEE improvement, scrap and rework reduction, maintenance cost reduction, energy savings.
- Number of equipment AI use cases moved from pilot to serial production per year, and replication across lines / plants.
- Reliability of deployed AI solutions in daily production (availability, alert precision / recall, model drift control).
- Equipment and maintenance team competency uplift: training sessions delivered, internal documentation produced, solutions independently operated after handover.
博世集团网上招聘数据处理同意声明
个人信息及目的：你的如下个人信息将被处理并用于招聘：姓名、电子邮箱、电话号码、所在地区、经验和教育信息、简历等应聘者自主提交的附件信息、证件号、职位、部门、计划入职日期、第一次劳动合同持续时间、工作地点、月薪、年薪、浮动奖金比例、餐贴、车贴。
博世将依据相关的数据保护法规，在全球范围内对您的数据进行保密。招聘流程中，您的个人信息只有在必须时才会被提供给博世以外的第三方机构(如代理机构)。博世精心挑选了第三方机构并与之签订了保密合同。博世采取了种种保密措施以保护您的数据，确保其不会遭受操纵、丢失、破坏、未经授权的访问或泄漏。我们的安全措施将随着新技术的应用而不断升级。您的本地浏览器和我们的人才招募系统之间的数据传输通过https加密。
特定职位申请：申请博世集团的职位就意味着您将自己的个人信息提供给博世集团旗下相应的法律实体（具体请见博世集团法人清单）。请注意，若有任何文件或资料变更，请在所有申请书里一并变更。人力资源部门的员工可能与您联系，询问您是否同意将您的求职申请转向博世集团内部另一合适职位。如果您申请了特定职位，那么只有在您同意的情况下，博世才会将您的求职申请转向另一职位。
您可以随时撤回求职申请，博世将根据相关法律删除您的个人信息。与您的申请相关的所有资料将被保留至24个月。若申请美国职位，博世依法保留三年。
您的权利：若您希望停止向博世提供您的个人信息，可联系我们或通过博世网上招聘数据隐私声明中的BKMS系统提出申请。您可以在SmartRecruiters平台查阅和修改您的简历。您对您的个人信息的处理享有知情权、决定权，您有权限制或者拒绝我们对您的个人信息进行处理。您还可以更正、补充您的个人信息。
跨境传输：为了实现前述目的，我们所收集的您的个人信息可能会在多个国家或地区间进行跨境转移，例如德国、新加坡。
德国：罗伯特-博世有限公司（德国格宁根市罗伯特博世广场1号（Robert-Bosch-Platz 1, Gerlingen-Schillerhohe, Germany，邮编：70839），用于集团统一招聘管理（博世中国与数据接收方均基于该目的开展数据出境活动；
英国：SmartRecruiters（英国伯克郡温莎亚瑟路圣斯蒂芬大厦 邮政区码: SL4 1RU），用于招聘、后期交流联系及分享职位/活动信息、潜在人才社区邀请。
您可通过上述联系方式依法向境外接收方行使您在个人信息处理活动中的权利。
存储您的个人信息：我们将仅在为实现目的必要的范围内以及法律法规要求的时间内保留您的个人信息。
雇佣：被雇佣后，您提供的数据将被从当前招聘系统转移至职位所属公司的人力资源管理系统。
本政策更新：我们将通过【邮件通知】的方式提供经更新的本政策，请您及时查看以获得更新。
联系方式：
请通过访问博世中国官方主页：博世在中国＞加入博世＞工作机会，在网上招聘数据隐私声明中“9.联系方式”
博世集团法人清单：
请通过访问博世中国官方主页：博世在中国＞加入博世＞工作机会，在网上招聘数据隐私声明中获取现阶段使用网上招聘系统的博世集团法人（第五页）
Qualifications Requirement
- Master’s degree (or above) in Computer Science, AI/ML, Electrical / Automation Engineering, Mechanical Engineering, or a closely related field; exceptional Bachelor candidates with a strong delivery record will also be considered.
- 3+ years of hands-on experience in real AI/ML projects, including responsibility for deploying AI solutions in an industrial or manufacturing environment (equipment, maintenance, or production-technology domain is a strong plus).
- Proven track record of solving real problems: AI solutions delivered from prototype to serial production with measurable business impact (uptime, scrap, cost, energy).
- Strong proactive, self-driven learning mindset with a customer-oriented and CIP (Continuous Improvement) attitude; willingness to spend substantial time on the shop floor.
Job Relevant Knowledge and Experience
AI & Machine Learning Engineering
- Expert in Python; proficient in PyTorch and/or TensorFlow/Keras and scikit-learn.
- Strong in time-series machine learning on machine / process data: forecasting, anomaly detection, failure prediction; solid applied-statistics and signal-processing fundamentals.
- Proficient in computer vision: OpenCV (filtering, feature extraction, camera calibration) and detection / segmentation models (YOLO, Faster-RCNN, SAM) applied to industrial inspection tasks.
- Hands-on LLM application engineering: RAG (vector databases, embedding pipelines, context management), prompt engineering, tool-calling; awareness of agentic patterns and frameworks (LangGraph, MCP) — applied selectively where they add real value.
- Familiar with data labelling toolchains and data versioning (Label Studio, CVAT, DVC, MLflow).
Equipment & Industrial Domain Knowledge
- Understanding of manufacturing equipment and automation: PLC/CNC, robotics, drives, sensors, and industrial communication (OPC UA, MQTT, Profinet/EtherCAT).
- Familiar with equipment and maintenance processes: TPM, condition monitoring, spare-parts management, OEE, MTBF/MTTR; able to turn failure modes and maintenance records into data problems.
- Experience with industrial data infrastructure: historians, IIoT platforms, and edge computing in production environments.
- Structured problem-solving methods (8D, DMAIC, PDCA); Six Sigma certification is a plus.
Deployment, MLOps & Edge Engineering
- Proficient in Git and CI/CD pipelines for ML/AI systems; containerisation (Docker, Kubernetes) for model serving.
- Model optimisation for edge deployment: quantisation, ONNX, TensorRT.
- MLOps practices: experiment tracking (MLflow, W&B), model registry, monitoring, and drift management.
- Familiar with cloud / on-prem ML platforms (Azure ML, AWS SageMaker, or equivalent); IT/OT security awareness.
Domain & Soft Skills
- Ability to go deep into the factory: observe real processes, talk to operators and maintainers, and translate ambiguous shop-floor pain into well-scoped, valuable AI solutions.
- Owns a strategy and defends it with evidence — including the discipline to say “no” to AI use cases without business value.
- Strong analytical and structured problem-solving skills; excellent communication with both shop-floor and management stakeholders.
- Intercultural collaboration experience; comfortable working in cross-functional, international teams.
- Mandarin (working proficiency) and English (professional proficiency) required.
Competency Related Requirements
(mark 3 most important characteristics of each sub-group — marked in bold)
Entrepreneurial Mindset
Goal/Profit Orientation Cost Awareness Consistency Efficient Implementation Resource Management Process Control Strategic Thinking/Action Market/Customer Orientation Innovation Initiative Willingness to Take Risks Change Management
Social Competence
Cooperation Ability to Create and Use Networks Openness Credibility Reliability Share Knowledge and Information Ability to Work in Teams Oral/Written Communications Presentation Skills Manage Group Processes Negotiation Skills Intercultural Skills Handling Conflicts Foreign Language Ability Integrity
Technical and Methodological Competence
Overview Cross-functional/International Experience Interdisciplinary Way of Thinking Flexibility Project Management Analytical/Intellectual Skills Specialist Knowledge Recognized Expertise Creativity Continuous Learning
Leadership (required for all supervisory positions, optional for project leader positions)
Ambition to Lead Conviction Strength of Character Energy Courage Optimism Stability Charisma Integrity Decision-making Strength Ability to Motivate/Animate Others Policy Deployment/Development of targets Delegation Assess Performance Give Feedback Develop Staff Recognize and Encourage Talent Mentoring/Coaching
Key Performance Indicators
- OTD (On-Time Delivery) of AI projects according to RBCD/TEF3 business requirements.
- Measurable business impact of deployed AI solutions: machine uptime / OEE improvement, scrap and rework reduction, maintenance cost reduction, energy savings.
- Number of equipment AI use cases moved from pilot to serial production per year, and replication across lines / plants.
- Reliability of deployed AI solutions in daily production (availability, alert precision / recall, model drift control).
- Equipment and maintenance team competency uplift: training sessions delivered, internal documentation produced, solutions independently operated after handover.
博世集团网上招聘数据处理同意声明
个人信息及目的：你的如下个人信息将被处理并用于招聘：姓名、电子邮箱、电话号码、所在地区、经验和教育信息、简历等应聘者自主提交的附件信息、证件号、职位、部门、计划入职日期、第一次劳动合同持续时间、工作地点、月薪、年薪、浮动奖金比例、餐贴、车贴。
博世将依据相关的数据保护法规，在全球范围内对您的数据进行保密。招聘流程中，您的个人信息只有在必须时才会被提供给博世以外的第三方机构(如代理机构)。博世精心挑选了第三方机构并与之签订了保密合同。博世采取了种种保密措施以保护您的数据，确保其不会遭受操纵、丢失、破坏、未经授权的访问或泄漏。我们的安全措施将随着新技术的应用而不断升级。您的本地浏览器和我们的人才招募系统之间的数据传输通过https加密。
特定职位申请：申请博世集团的职位就意味着您将自己的个人信息提供给博世集团旗下相应的法律实体（具体请见博世集团法人清单）。请注意，若有任何文件或资料变更，请在所有申请书里一并变更。人力资源部门的员工可能与您联系，询问您是否同意将您的求职申请转向博世集团内部另一合适职位。如果您申请了特定职位，那么只有在您同意的情况下，博世才会将您的求职申请转向另一职位。
您可以随时撤回求职申请，博世将根据相关法律删除您的个人信息。与您的申请相关的所有资料将被保留至24个月。若申请美国职位，博世依法保留三年。
您的权利：若您希望停止向博世提供您的个人信息，可联系我们或通过博世网上招聘数据隐私声明中的BKMS系统提出申请。您可以在SmartRecruiters平台查阅和修改您的简历。您对您的个人信息的处理享有知情权、决定权，您有权限制或者拒绝我们对您的个人信息进行处理。您还可以更正、补充您的个人信息。
跨境传输：为了实现前述目的，我们所收集的您的个人信息可能会在多个国家或地区间进行跨境转移，例如德国、新加坡。
德国：罗伯特-博世有限公司（德国格宁根市罗伯特博世广场1号（Robert-Bosch-Platz 1, Gerlingen-Schillerhohe, Germany，邮编：70839），用于集团统一招聘管理（博世中国与数据接收方均基于该目的开展数据出境活动；
英国：SmartRecruiters（英国伯克郡温莎亚瑟路圣斯蒂芬大厦 邮政区码: SL4 1RU），用于招聘、后期交流联系及分享职位/活动信息、潜在人才社区邀请。
您可通过上述联系方式依法向境外接收方行使您在个人信息处理活动中的权利。
存储您的个人信息：我们将仅在为实现目的必要的范围内以及法律法规要求的时间内保留您的个人信息。
雇佣：被雇佣后，您提供的数据将被从当前招聘系统转移至职位所属公司的人力资源管理系统。
本政策更新：我们将通过【邮件通知】的方式提供经更新的本政策，请您及时查看以获得更新。
联系方式：
请通过访问博世中国官方主页：博世在中国＞加入博世＞工作机会，在网上招聘数据隐私声明中“9.联系方式”
博世集团法人清单：
请通过访问博世中国官方主页：博世在中国＞加入博世＞工作机会，在网上招聘数据隐私声明中获取现阶段使用网上招聘系统的博世集团法人（第五页）
