AI Expert_PS

Bosch Powertrain Systems Co., Ltd. (RBCD), the joint venture of Robert Bosch GmbH and Weifu High Technology Group Co., Ltd., is a high tech enterprise specializing in the development, production and sales of common rail systems, exhaust gas after treatment systems, fuel cell stacks and key components. The company, based on its local R&D and project management competence, with innovative technologies, can support customers to continuously improve internal combustion engines efficiency, to reduce emission, and to accelerate the market launch of new energy products. The garget of the company is to provide the Chinese market and customers with diversified advanced powertrain products.

Job Summary

As an AI Expert in RBCD/TEF4, you will be a core builder of Bosch's AI-Native Operations in BBM-CN. You will design and develop Agentic AI systems – autonomous AI agents that work side-by-side with our operations teams to continuously improve manufacturing and business processes through the M.AI.Co (Mobility AI Copilot) platform. Beyond classical computer vision and deep learning, you will own the full agentic development cycle: from opportunity identification and agent architecture, to data governance, safety guardrails, and production-grade deployment.

Roles and Responsibilities
- Agentic AI Development
- Identify agentic opportunities across RBCD manufacturing operations; translate business cases into technical agent architectures and implementation roadmaps.
- Design, develop, and deploy AI agents using modern agentic frameworks (LangGraph, AutoGen, CrewAI, or equivalent) integrated into the M.AI.Co platform.
- Leverage Coding Agents (Claude Code, OpenAI Codex, GitHub Copilot Workspace) to accelerate agent and tool development; apply prompt engineering, tool-use, and multi-agent orchestration patterns.
- Architect multi-agent pipelines that combine LLMs, vision models, retrieval-augmented generation (RAG), and structured data to deliver end-to-end automated workflows.
- Build and maintain safety guardrails: human-in-the-loop checkpoints, output validation, audit logging, and risk control frameworks for production agent deployments.
- Manage agent data assets — define retrieval strategy, knowledge-base architecture, and context window optimization to ensure agents have accurate, up-to-date information.
- Computer Vision & Classical AI
- Lead end-to-end development of computer vision and deep learning algorithms for AOI (Automatic Optical Inspection) and manufacturing quality processes — from PoC to serial implementation.
- Design and implement model architectures (CNN, Transformer-based ViT, multimodal) for classification, detection, segmentation, and anomaly detection tasks.
- Establish data collection, labelling, and data management strategies to enable fast model iteration and continuous performance improvement.
- Collaborate with IT, machine, and product technical experts to define data pipelines, storage concepts, and MLOps practices.
- Leadership & Knowledge Sharing
- Lead or accompany cross-functional project teams, taking full responsibility for costs, quality, and on-time delivery.
- Coordinate AI work packages within RBCD manufacturing; cooperate with PS central departments to deploy and share standard solutions across plants.
- Coach and mentor data enthusiasts and team members; drive AI competency build-up including Agentic AI literacy across the organisation.
- Proactively monitor the latest AI developments (foundation models, agentic frameworks, coding agents) and evaluate their applicability to RBCD use cases. Authority ( mandatory for Grade 7 and above, optional for others )
- Lead or develop analysis model, algorithms for improvement projects with full responsibility for costs, quality, and on-time delivery.
- Cooperate with PS central department to deploy / sharing the standard solutions.
- Coaching and guiding data enthusiasm and team members supporting them competency build up

- Master's degree (or above) in Computer Science, Computer Vision, AI/ML, Electrical Engineering, or a closely related field.
- 3+ years of hands-on experience in real AI/ML projects, with at least 1 year in Agentic AI, LLM application development, or equivalent.
- Proven track record of delivering AI solutions from prototype to production in an industrial or manufacturing environment.
- Strong proactive, self-driven learning mindset with a customer-oriented and CIP (Continuous Improvement) attitude. Job Relevant Knowledge and Experience

- Agentic AI & LLM Engineering
- Hands-on experience with Coding Agents: Claude Code (Anthropic), OpenAI Codex / Codex CLI, GitHub Copilot Workspace, or Cursor.
- Experience building and running multi-agent systems with LangGraph, AutoGen, CrewAI, or similar agentic orchestration frameworks.
- Proficient in LLM API integration (OpenAI, Anthropic Claude, Azure OpenAI, open-source LLMs via Ollama / vLLM).
- Skilled in Retrieval-Augmented Generation (RAG): vector databases (Chroma, Weaviate, Qdrant, pgvector), embedding pipelines, context management.
- Familiar with prompt engineering patterns: chain-of-thought, few-shot, tool-calling, structured output, and self-reflection loops.
- Understanding of agent safety: output guardrails, hallucination detection, human-in-the-loop design, audit trails.
- Familiar with MCP (Model Context Protocol) or comparable agent-tool integration standards.
 

- Computer Vision & Deep Learning
- Expert in Python; proficient in PyTorch and/or TensorFlow/Keras.
- Strong in classical image processing (OpenCV): filtering, edge detection, feature extraction, camera calibration.
- Deep understanding of CNN architectures: AlexNet, VGG, ResNet, DenseNet, MobileNet, EfficientNet.
- Proficient in object detection (YOLO, Faster-RCNN, SSD, DETR) and segmentation (Mask-RCNN, SAM).
- Experience with Vision Transformers (ViT, Swin) and multimodal models (CLIP, LLaVA, GPT-4V).
- Familiar with data labelling toolchains (Label Studio, CVAT) and data versioning (DVC, MLflow).
- Knowledge of model optimisation for edge deployment: quantisation, pruning, ONNX, TensorRT.
 

- MLOps & Engineering Practices
- Proficient in Git and CI/CD pipelines for ML/AI systems.
- Experience with containerisation (Docker, Kubernetes) for agent and model serving.
- Familiar with cloud/on-prem ML platforms (Azure ML, AWS SageMaker, or equivalent).
- Experience with experiment tracking (MLflow, W&B) and model registry practices.
 

- Domain & Soft Skills
- Ability to translate ambiguous manufacturing/business problems into well-scoped AI solutions.
- Strong analytical and structured problem-solving skills.
- Excellent communication skills — able to present complex AI topics to non-technical stakeholders.
- Intercultural collaboration experience; comfortable working in cross-functional, international teams.
- Mandarin (working proficiency) and English (professional proficiency) required.