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.
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.
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