Lead Computer Vision Engineer

Job Summary :

We seek an AI expert to enhance our technological capabilities and focus on developing and optimizing advanced computer vision algorithms for applications in automatic target recognition, detection, classification, and segmentation. The candidate should have a strong foundation in machine learning, AI, and sensor fusion and practical experience in image processing, feature extraction, and real-time edge AI models.

Key Accountabilities :

• Develop and implement advanced computer vision algorithms for automatic target recognition (ATR), detection, classification, and segmentation tasks.
• Familiarity with tracking algorithms such as correlation and deep learning trackers.
• Utilize feature matching, optical flow, homography, and warping techniques to enhance image analysis and processing capabilities.
• Implement image processing algorithms for tasks like restoration and denoising, ensuring superior image quality and clarity.
• Utilize feature extraction methods and pattern recognition techniques to extract meaningful information from images.
• Background on speed and depth estimation techniques.
• Develop edge AI models optimized for real-time applications, leveraging edge computing platforms like NVIDIA Jetson.
• Utilize optimization techniques, parallel computing, GPU acceleration, and distributed computing to improve the performance and efficiency of algorithms.
• Utilize inference engines to optimize model performance and accelerate computations.
• Familiarity with ONNX for model interoperability across different frameworks
• Work on sensor fusion techniques to enhance perception capabilities, including multimodal systems, lidar, and camera fusion.
• Synthetic data generation 3D modeling, simulation environments, and AI techniques such as generative AI models (GANs, Variational Autoencoders (VAEs), etc.)
• Analyze datasets, implement data augmentation techniques, and ensure data security through encryption algorithms.
• Explore and implement advanced techniques such as CNNs, RNNs, LSTMs, and Explainable AI.
• Knowledge in Vision transformers and attention mechanisms.
• Experience with NVIDIA DeepStream for video analytics and streaming applications.
• Experience in 3D reconstruction techniques, including stereo vision, structure from motion (SfM), and photogrammetry for spatial analysis and reconstruction.
• Familiarity with VISNIR, MWIR, and SWIR imaging technologies for specialized applications.
• Proficiency in Linux environments for development and deployment.
• Proficiency in languages such as Python, C / C++, MATLAB, and frameworks like TensorFlow, Keras, and PyTorch
• Knowledge of Docker containers for packaging and deploying computer vision application
• Implement benchmarking techniques to evaluate the efficiency and effectiveness of algorithms and models.

Experience & Education Qualifications :