Engineering Trainee, Process AI & Data Analyst (Based in Jiangmen)

Key Responsibilities

1. Injection Process AI & Closed-Loop Setup

• Lead the design, development, and deployment of AI-based closed-loop process control for injection molding machines.
• Drive the project rollout for plastic gear and water pump component application, ensuring measurable improvements in capability (Cp/Cpk) and defect reduction.
• Integrate real-time process signals, sensor data, and quality feedback to build self-optimizing process loops.
• Collaborate with tooling, quality, and production teams to validate AI models on production lines.

2. E2E Injection Process Data Application & JEB Enablement

• Architect and implement an end-to-end data pipeline for injection molding process data — from machine/sensor acquisition through storage, analysis, visualization, and decision support.
• Build dashboards and reporting tools to visualize process performance, OEE, capability trends, and AI model outputs.
• Educate and mentor the JEB team (process engineers, technicians, operators) on how to interpret and act on data in their daily work — shifting from instinct-driven to data-driven decision making.
• Create standard operating procedures (SOPs), training materials, and hands-on workshops for sustaining the data culture.

3. Team Capability Building (Software & AI Skills)

• Drive technical upskilling of the engineering team across the following software and AI tool stack:

  • Python – for data analysis, scripting, automation, and ML model development.
  • SQL – for querying manufacturing databases, process historians, and MES systems.
  • LLM / RAG (Large Language Models & Retrieval-Augmented Generation) – for building knowledge assistants, troubleshooting guides, and document intelligence tools.
  • Azure AI Studio – for developing, deploying, and managing AI models in the cloud.
  • Power BI – for creating interactive process dashboards and real-time visual analytics.
  • Manufacturing Process Knowledge – deep understanding of injection molding, material science, and process optimization.

• Lead at least 1–2 full-cycle AI project deployments into production, managing the end-to-end lifecycle from problem definition, data collection, model training, validation, deployment, and monitoring.

4. Cross-Functional Collaboration & Innovation

• Partner with IT, Data Engineering, and OT (Operational Technology) teams to ensure seamless data connectivity between shop floor systems and cloud/analytics platforms.
• Identify new opportunities for AI/ML application in manufacturing (e.g., predictive maintenance, defect classification, yield optimization).
• Stay current with emerging trends in AI for manufacturing and introduce relevant innovations to the organization.
• Document project outcomes, best practices, and lessons learned for organizational knowledge retention.

Required Qualifications & Skills

• Bachelor's degree or higher in Mechanical Engineering, Manufacturing Engineering, Computer Science, Data Science, Electrical Engineering, or a related technical field.
• Fresh graduate are welcome.

What we offer

• Friendly and stable working environment

• Comprehensive on-the-job training, engaging classroom sessions, and an extensive e-learning system

• Global exposure with excellent career development opportunities

• Free accommodation and shuttle bus provided by company