Nanolumi is an advanced materials company that delivers breakthrough luminescent solutions for Product Authentication (Reyal™) and Bio-imaging. The company does this by harnessing cutting-edge nano / molecular chemistries and optical system to create, detect, and manipulate light.

The team’s expertise lies in identifying & optimising technical synergies, building comprehensive solutions through integrations & partnerships, and fostering supply chain collaborations to accelerate new technology adoption.

Nanolumi firmly believes that materials matter and play a pivotal role in empowering products that enable society to experience life to the fullest.

Project Role: Image Recognition and Machine Learning Application Development Engineer

The Image Recognition and Machine Learning Application Development Engineer typically involves a range of tasks related to developing, implementing, and optimising algorithms and systems for analysing and interpreting visual image data using machine learning techniques for anti-counterfeit authorisation in the smartphone platform. Day-to-day operation support and continuous improvement in the image recognition algorithm are also part of the job portfolio.

Trainee's responsibilities include but are not limited to:

• Algorithm and model development for image recognition and analysis.
• Large datasets of images collection and pre-processing for training machine learning models.
• Model training and evaluation and deep learning frameworks establishment. 
• Model optimisation and deployment to production environments such as cloud platforms, edge devices, or embedded systems.
• Feature engineering from images to improve model performance and interpretability.
• Understanding the specific anti-counterfeit application area where image recognition is being applied and tailoring algorithms and models accordingly.
• Model evaluation and validation by using appropriate metrics and validation techniques to ensure the accuracy, robustness, and reliability of the image recognition system.
• Model deployment and integration into production environments. Ensure seamless integration with existing infrastructure and maintain compatibility with different platforms and frameworks.
• Monitor the performance of deployed models and systems, and identifying and addressing challenges and limitations in existing algorithms or systems. Troubleshooting any issues or anomalies that arise, and debugging issues related to data quality, model performance, or system integration. Perform regular maintenance and updates to keep the system up-to-date and functioning optimally.