Job Overview

We are seeking a highly skilled Deep Learning Engineer with expertise in large-scale image analysis to join our team. The ideal candidate will work with a large-scale dataset, focusing on object detection and RCNN-based deep learning models for identification and localization. This role requires strong proficiency in deep learning, computer vision, and experience handling large datasets.

Job Responsibilities

• Develop, implement, and optimize RCNN-based object detection models (Fast R-CNN, Faster R-CNN, Mask R-CNN, etc.).
• Design and train deep learning models for detection, segmentation, and classification in images.
• Handle and preprocess large-scale image datasets.
• Implement data augmentation and transfer learning techniques to improve model performance.
• Work with GPU-accelerated training frameworks (PyTorch, TensorFlow, CUDA, etc.).
• Develop pipelines for data annotation, preprocessing, and augmentation.
• Conduct error analysis and model interpretability studies to improve model reliability.
• Collaborate with domain experts to fine-tune models.
• Deploy deep learning models into production using cloud services or on-premise infrastructure.
• Stay updated with state-of-the-art research in AI and computer vision.
  
  

Requirements

• MSc/PhD in Computer Science, Data Science, AI, or a related field.
• 3+ years of experience in deep learning and computer vision.
• Strong proficiency in Python, PyTorch, TensorFlow, OpenCV.
• Experience with object detection and segmentation models, particularly RCNN variants.
• Solid understanding of convolutional neural networks (CNNs), transformers, and generative models.
• Experience with distributed computing and handling large datasets.
• Knowledge of model deployment and optimization (ONNX, TensorRT, Kubernetes, AWS/GCP/Azure).
• Strong problem-solving skills and ability to work with cross-functional teams.