Fake Damage Recognition Specialist

Overview:

Key Responsibilities:

• Design and develop machine learning models for recognizing fake and damaged items.
• Implement computer vision techniques for imagebased classification and identification.
• Collaborate with software developers to integrate recognition algorithms into production systems.
• Analyze and interpret data to improve recognition accuracy and efficiency.
• Research and evaluate new technologies and methodologies to enhance recognition capabilities.
• Contribute to the design and development of data pipelines for preprocessing and feature extraction.
• Collaborate with quality assurance teams to validate recognition performance against predefined metrics.
• Optimize algorithms for realtime processing and performance.
• Document technical specifications process workflows and algorithm performance.
• Participate in crossfunctional meetings to provide updates and insights on recognition development.
• Conduct code reviews and provide constructive feedback to team members.
• Support the deployment and maintenance of recognition systems in production environments.

Required Qualifications:

• General improvements support for the application:
• Windows and Linux service support.
• GPU and CPU code support.
• Support for standard CUDA library build.
• Ability to handle multiple services per machine with a preference for GPU activation only when needed.

• Comprehensive experience in fake recognition technologies.
• Proficient in identifying and correcting existing bugs in the RIF application
• Capability to handle damage recognition functionalities.

machine learning,computer vision,python,image processing,gpgpu,cuda