Computer Vision

As a Lead Computer Vision Engineer, you will lead the development and deployment of cutting-edge computer vision models and solutions for a variety of applications including image classification, object detection, segmentation, and more. You will work closely with cross-functional teams to implement advanced computer vision algorithms, ensure the integration of AI solutions into products, and help guide the research and innovation of next-generation visual AI technologies.

• Stay up-to-date with the latest advancements in computer vision, deep learning, and AI, and apply this knowledge to push the boundaries of current solutions.
• Conduct research to advance the field of computer vision, including contributing to the development of new algorithms, frameworks, or methodologies.
• Design and implement novel computer vision models and algorithms for tasks such as object detection, image segmentation, optical character recognition (OCR), and facial recognition.
• Utilize large datasets to train, test, and improve models, optimizing for both performance and efficiency.
• Develop end-to-end computer vision pipelines, from data acquisition and preprocessing to model training, testing, and deployment.
• Optimize models for both accuracy and real-time performance, leveraging tools such as Tensor Flow, Py Torch, and OpenCV.
• Deep Learning Frameworks: Proficiency in Tensor Flow, Py Torch, or other deep learning libraries.
• Computer Vision Tools: Expertise in OpenCV, Dlib, and other image processing libraries.
• Model Deployment: Experience deploying models to production using platforms such as AWS, Google Cloud, or NVIDIA Jetson (for edge devices).
• Algorithms: Strong understanding of core computer vision techniques like image classification, object detection (YOLO, Faster R-CNN), image segmentation (U-Net), and feature extraction.
• Programming Languages: Proficient in Python, C++, and other relevant programming languages for computer vision tasks.
• Data Handling: Experience working with large datasets, data augmentation, and preprocessing techniques.
• Optimization: Skills in model optimization techniques such as pruning, quantization, and hardware acceleration (e.g., using GPUs or TPUs).
• Experience with state-of-the-art computer vision techniques like GANs, reinforcement learning for vision tasks, and self-supervised learning.
• Familiarity with edge-computing frameworks (e.g., TensorRT, NVIDIA Deep Stream) for deploying models on embedded systems.
• Contributions to open-source projects or published research papers in computer vision or related fields.

Education: Bachelor Of Comp. Applications,Bachelor Of Computer Science,Bachelor Of Science,Bachelor of Engineering,Bachelor Of Technology

Preferred skills: Technology->data science->Computer Vision