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Chief Biometric Systems Architect
2 weeks ago
About Us:
We are a leading provider of risk assessment solutions.
Our Business Services vertical offers innovative solutions to drive revenue growth, maximize operational efficiencies, and improve customer experience.
Our Team:
We have a strong team of passionate engineers building a world-class platform to fight identity fraud at a global scale.
The Role:
This role will focus on developing advanced systems to detect and prevent spoofing attacks during biometric authentication processes, utilizing machine learning and deep learning techniques.
Key Responsibilities:
- 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, selecting appropriate algorithms and optimizing model parameters to enhance accuracy and reliability.
- Feature Engineering: Identify and extract features from biometric data crucial for detecting spoofing attempts, including texture analysis, motion-based detection, and 3D depth analysis.
- 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, validating models against various spoofing techniques.
- Monitoring and Maintenance: Deploy liveness detection systems into production environments, ensuring scalability and high performance, continuously monitoring system outputs to identify any issues with accuracy or efficiency.
Requirements:
- Machine Learning Expertise: In-depth understanding of machine learning frameworks such as TensorFlow, Keras, or PyTorch, with experience in developing deep learning models.
- 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 to achieve common goals.
- Experience in similar roles focusing on anti-spoofing or biometric security systems.
