Job
Description
As a Deep Learning Engineer, you will be responsible for developing, training, and deploying computer vision models to tackle complex visual challenges. You will engage in cutting-edge technology within image processing, object detection, and video analysis, collaborating with diverse teams to create impactful real-world applications. - Develop and optimize computer vision models for object detection (YOLO, Faster R-CNN, SSD) and image classification (ResNet, MobileNet, EfficientNet, ViTs). - Work with OCR technologies (Tesseract, EasyOCR, CRNN, TrOCR) for text extraction from images. - Utilize PyTorch, TensorFlow, and OpenCV for deep learning and image processing tasks. - Implement sequence-based models such as RNNs, LSTMs, and GRUs for vision-related tasks. - Optimize software for real-time performance on multiple platforms. - Implement and deploy AI models using Flask/FastAPI and integrate with SQL/NoSQL databases. - Utilize Git/GitHub for version control and team collaboration. - Apply ML algorithms like regression, decision trees, and clustering as required. - Review code, mentor team members, and enhance model efficiency. - Stay updated with advancements in deep learning and multimodal AI. **Qualifications Required:** - Proficiency in Python for AI development. - Experience with PyTorch, TensorFlow, and OpenCV. - Knowledge of object detection (YOLO, Faster R-CNN, SSD) and image classification (ResNet, MobileNet, EfficientNet, ViTs). - Experience with OCR technologies (Tesseract, EasyOCR, CRNN, TrOCR). - Familiarity with RNNs, LSTMs, GRUs for sequence-based tasks. - Experience with Generative Adversarial Networks (GANs) and Diffusion Models for image generation. - Understanding of REST APIs (Flask/FastAPI) and SQL/NoSQL databases. - Strong problem-solving and real-time AI optimization skills. - Experience with Git/GitHub for version control. - Knowledge of Docker, Kubernetes, and model deployment at scale on serverless and on-prem platforms. - Understanding of vector databases (FAISS, Milvus). This job requires proficiency in deep learning, computer vision, and AI technologies, along with the ability to collaborate effectively with cross-functional teams to drive impactful solutions. As a Deep Learning Engineer, you will be responsible for developing, training, and deploying computer vision models to tackle complex visual challenges. You will engage in cutting-edge technology within image processing, object detection, and video analysis, collaborating with diverse teams to create impactful real-world applications. - Develop and optimize computer vision models for object detection (YOLO, Faster R-CNN, SSD) and image classification (ResNet, MobileNet, EfficientNet, ViTs). - Work with OCR technologies (Tesseract, EasyOCR, CRNN, TrOCR) for text extraction from images. - Utilize PyTorch, TensorFlow, and OpenCV for deep learning and image processing tasks. - Implement sequence-based models such as RNNs, LSTMs, and GRUs for vision-related tasks. - Optimize software for real-time performance on multiple platforms. - Implement and deploy AI models using Flask/FastAPI and integrate with SQL/NoSQL databases. - Utilize Git/GitHub for version control and team collaboration. - Apply ML algorithms like regression, decision trees, and clustering as required. - Review code, mentor team members, and enhance model efficiency. - Stay updated with advancements in deep learning and multimodal AI. **Qualifications Required:** - Proficiency in Python for AI development. - Experience with PyTorch, TensorFlow, and OpenCV. - Knowledge of object detection (YOLO, Faster R-CNN, SSD) and image classification (ResNet, MobileNet, EfficientNet, ViTs). - Experience with OCR technologies (Tesseract, EasyOCR, CRNN, TrOCR). - Familiarity with RNNs, LSTMs, GRUs for sequence-based tasks. - Experience with Generative Adversarial Networks (GANs) and Diffusion Models for image generation. - Understanding of REST APIs (Flask/FastAPI) and SQL/NoSQL databases. - Strong problem-solving and real-time AI optimization skills. - Experience with Git/GitHub for version control. - Knowledge of Docker, Kubernetes, and model deployment at scale on serverless and on-prem platforms. - Understanding of vector databases (FAISS, Milvus). This job requires proficiency in deep learning, computer vision, and AI technologies, along with the ability to collaborate effectively with cross-functional teams to drive impactful solutions.
