1. Computer Vision Model Development
- Design, develop, and optimize advanced computer vision and deep learning models for industrial applications such as defect detection, classification, and process control.
- Apply state-of-the-art algorithms and continuously improve model performance through validation, tuning, and retraining.
- Drive the transition of models from proof-of-concept to production-ready solutions. 2. Data Engineering & Pipeline Management
- Own the end-to-end data lifecycle, including acquisition, annotation, preprocessing, and dataset management.
- Ensure data robustness and quality under real industrial conditions (e.g., lighting, materials, positioning variability).
- Collaborate on scalable data and deployment pipelines supporting efficient training and inference. 3. Industrial Deployment & System Integration
- Deploy computer vision models into real-time industrial systems integrated with production lines and automation equipment.
- Define and validate optimal hardware configurations (cameras, optics, lighting, edge devices).
- Ensure deployed systems meet industrial standards for performance, reliability, and maintainability. 4. Performance Monitoring & Continuous Optimization
- Define and track key performance indicators (e.g., accuracy, precision, recall, latency, uptime).
- Analyze system behavior in production and implement improvements to enhance robustness and efficiency.
- Troubleshoot operational issues and optimize performance under real-world constraints. 5. Cross-functional Collaboration & Scaling
- Work closely with production, quality, IT/OT, and engineering teams to ensure successful deployment and adoption.
- Support scaling of computer vision solutions across plants and business units.
- Engage with internal and external stakeholders (local institutions, Suppliers) to accelerate industrial AI implementation.
