AI Expert_PS
On-site · Wuxi, Jiangsu, China
Job Summary
Hands-on AI leadership role focused on designing and deploying Agentic AI systems to optimize manufacturing and business processes. Responsible for end-to-end agent development lifecycle from opportunity identification and architecture to data governance, safety guardrails, and production deployment on the M.AI.Co platform. Lead multi-agent pipelines integrating LLMs, vision models, RAG, and structured data; leverage Coding Agents (Claude Code, OpenAI Codex, GitHub Copilot) to accelerate development; establish robust safety and audit trails; oversee data assets and retrieval strategies. Drive end-to-end computer vision and classical AI work for AOI and manufacturing quality, defining model architectures (CNNs, ViT, multimodal) and data collection/labeling strategies. Collaborate with IT, manufacturing, and product teams to define data pipelines, storage concepts, and MLOps practices. Lead or accompany cross-functional projects, mentor data enthusiasts, and stay abreast of AI advances (foundation models, agentic frameworks, coding agents). Requires a Master’s degree (or above) in a related field, 3+ years of hands-on AI/ML experience with at least 1 year in agentic AI/LLM development, and strong communication skills in Mandarin and English.
Required Qualifications
- Master's degree (or above) in Computer Science, Computer Vision, AI/ML, Electrical Engineering, or closely related field
- 3+ years hands-on AI/ML experience with at least 1 year in Agentic AI, LLM application development, or equivalent
- Proven track record delivering AI solutions from prototype to production in industrial/manufacturing environments
- Strong proactive learning mindset, customer-oriented CIP attitude
- Hands-on experience with Coding Agents: Claude Code, OpenAI Codex, GitHub Copilot Workspace or Cursor
- Experience building and running multi-agent systems with LangGraph, AutoGen, CrewAI, or similar frameworks
- Proficient in LLM API integration (OpenAI, Anthropic Claude, Azure OpenAI, or open-source via Ollama/vLLM)
- Familiar with Retrieval-Augmented Generation (RAG) including vector databases (Chroma, Weaviate, Qdrant, pgvector) and embedding pipelines
- Prompt engineering patterns (chain-of-thought, few-shot, tool-calling)
- Understanding of agent safety: guardrails, hallucination detection, human-in-the-loop design
- Knowledge of MCP or similar agent-tool integration standards
- Python proficiency; PyTorch and/or TensorFlow/Keras
- OpenCV and classical image processing skills
- Experience with vision models (YOLO, Detectron/DETR, CNNs, ViT) and multimodal models (CLIP, LLaVA)
- Familiar with data labeling tools (Label Studio, CVAT) and ML ops (MLflow, DVC, MLflow)
- Experience with Docker/Kubernetes, cloud/on-prem platforms (Azure ML, AWS SageMaker)
- Ability to translate manufacturing problems into AI solutions
- Excellent communication and intercultural collaboration
- Mandarin and English proficiency
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