Job Description

Responsibilities: Demonstrated experience in computer vision, AI model development, and optimization. Experience working with medical videos, including MRI, endoscopy, ultrasound, echocardiograms, and OCR-based recognition. Proficiency in multimodal AI, integrating various medical imaging sources. Experience working closely with healthcare professionals and hospital workflows. Experience integrating AI models with hospital IT systems, PACS, and DICOM-based workflows. Proficiency in Python and experience with AI frameworks such as PyTorch, TensorFlow, OpenCV. Expertise in computer vision techniques, including Object detection (YOLO, SSD, Faster R-CNN), Image segmentation (U-Net, Mask R-CNN), Image classification (ResNet, EfficientNet, ViTs), Feature extraction (SIFT, SURF, ORB) Strong knowledge of machine learning techniques including Supervised, unsupervised, and self-supervised learning, CNNs, Vision Transformers (ViTs), GANs, attention-based networks, Random forests, SVMs, boosting algorithms Proficiency in data preprocessing, augmentation, normalization, and handling large-scale image datasets. Experience working with multi-GPU workloads for training and inference. Experience deploying models using containerization technologies (Docker, Kubernetes). Experience with high-performance computing (HPC) techniques for managing large-scale datasets. Background in federated learning for medical AI to enhance privacy-preserving model training. Prior experience in developing AI solutions for real-time clinical applications. Strong understanding of regulatory constraints in AI-driven medical applications. Ability to effectively communicate complex AI models to technical and non-technical stakeholders.

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