Key Responsibilities
1. Intelligent Defect Analysis & Containment
- CV-Driven Inspection : Develop and maintain Computer Vision models for real-time automated defect recognition on the production line.
- QDA Automation: Architect an automated QDN Lot Containment system that triggers immediate holds or re-routing of suspected sub-standard lots based on AI-detected anomalies.
- Real Time Commonality Analysis: Design systems that perform live commonality analysis across multiple equipment sets and batches to pinpoint root causes of defects as they occur.
2. System Integration & Optimization
- End-to-End Automation: Build seemless data pipelines that connect equipment level sensors to factory MES (Manufacturing Execution Systems) for instantaneous decison making.
- MLOps Lifecycle: Manage the deployment, monitoring and retraining of ML models to ensure high accuracy and low false positive rates in a live production environment.
- Dashboard & Reporting: Create real time visibility tools for engineering teams to monitor lot status and containment efficiency.
Required Skills & Qualifications
- Bachelor Degree in Automation Engineering, Computer Science, Data Science, or related technical field.
- Proven experience in building and deploying Machine Leaning models for classification and anomaly detection.
- High level of competence in Python (for AI/ML) and SQL (for data extraxtion/analysis)
- Familiarity with QDN processes, lot tracking and semiconductors/electronics manufacturing flows is highly preferred.
Pay: RM3,000.00 - RM5,000.00 per month
Work Location: In person