Liveness Detection System Design: Develop and design liveness detection systems that utilize AI algorithms to differentiate between real and fake biometric data.
Deep Learning Model Development: Build and optimize deep learning models specifically for liveness detection.
Feature Engineering: Identify and extract features from biometric data that are crucial for detecting spoofing attempts.
Data Collection and Preprocessing: Collaborate with data scientists to collect, clean, and preprocess large datasets required for training liveness detection models.
Algorithm Implementation: Implement machine learning algorithms capable of processing real time biometric data to detect inconsistencies indicative of spoofing attempts.
System Testing and Validation: Conduct rigorous testing of liveness detection systems to ensure performance in real-world scenarios.
Monitoring and Maintenance: Deploy liveness detection systems into production environments, ensuring scalability and high performance.

Machine Learning Expertise: In-depth understanding of machine learning frameworks such as TensorFlow, Keras, or PyTorch.
Programming Skills: Proficiency in programming languages such as Python, Java, or R for model development and algorithm implementation.
Analytical Skills: Strong problem-solving abilities with a solid grasp of statistics, probability theory, and data analysis techniques.
Collaboration Skills: Ability to work effectively with cross-functional teams (including data scientists, software engineers, and product managers) to achieve common goals.
Experience in similar roles focusing on anti-spoofing or biometric security systems.
Bachelor's degree in Computer Science, Mathematics, or a related field.
Familiarity with anti-spoofing standards for biometrics such as NIST ISO/IEC 30107 or FIDO.
Innovative mindset with a passion for continuous learning and keeping up with the latest advancements in AI and machine learning technologies.

Senior Software Engineer – Computer Vision

Key skills