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Lead Computer Vision Engineer
3 months ago
Lead and mentor a team of computer vision engineers, providing technical guidance and support.Project Management:
Oversee the entire project lifecycle, from requirements gathering to deployment and maintenance.Solution Architecture:
Design, develop, and implement robust computer vision algorithms and models to address complex image analysis challenges.Client Interaction:
Build strong relationships with clients, understand their needs, and translate them into technical requirements.Research and Development:
Know-how of the latest advancements in computer vision and machine learning, exploring new technologies and techniques.Model Development:
Develop, train, and deploy state-of-the-art computer vision models, including deep learning architectures like vision transformers.Data Analysis:
Analyze and interpret large datasets to extract meaningful insights and improve model performance.Performance Optimization:
Optimize computer vision models for speed, accuracy, and efficiency.QualificationsB.Tech or master's in computer science, Machine Learning, or a related field.Minimum 7 years of overall experience in software development and minimum 3-4 years of hands-on experience in computer vision and image analytics.Proven experience in leading and mentoring technical teams.Strong proficiency in Python, C++, or other relevant programming languages.Expertise in deep learning frameworks such as TensorFlow, PyTorch, or Keras.In-depth knowledge of computer vision techniques, including image processing, object detection, image segmentation, and image recognition.Experience with cloud platforms (AWS, GCP, Azure) and GPU computing.Strong problem-solving and analytical skills.Work with data engineers and analysts to source, analyse & engineer features, perform data augmentation.Excellent communication and interpersonal skills.Experience with vision transformers and other cutting-edge computer vision models is a plus.Experience on Fraud analytics is preferred.
Key competencies:Image Processing: Filtering, enhancement, restoration, geometric transformationsFeature Extraction: SIFT, SURF, HOG, LBP, and other traditional methods.Object Detection: Faster R-CNN, YOLO, SSD, RetinaNet, and their variants.Image Segmentation: U-Net, Deep Lab, Mask R-CNN.Generative Adversarial Networks (GANs): Image generation, style transfer.Attention Mechanisms: Transformer-based models, Vision Transformers.Image Classification: Convolutional Neural Networks (CNNs), ResNet, VGG, Inception.Image Registration: Aligning images from different sources.Data Preprocessing: Data cleaning, augmentation, normalization.Model Evaluation: Metrics, hyperparameter tuning, cross-validation.
