Computer Vision Analytics Engineer – Medical Video/Image Analytics Job Description: We are seeking Computer Vision Analytics Engineers to support a Medical Video Analytics Project. This initiative integrates real-time medical video processing, AI-powered computer vision, and cloud-based analytics to enhance endoscopic procedures and MRI imaging. The role involves working on edge-to-cloud video processing pipelines, developing vision algorithms for real-time object detection, and building machine learning models that generate automated insights and recommendations for medical professionals. Key Responsibilities: • Work with real-time video feeds from robotic-assisted surgery and endoscopic procedures. • Support remote and in-hospital control workflows for AI-enhanced video analytics. • Process and analyze high-speed medical video streams at gigabit-per-second (Gbps) throughput. • Ensure secure transmission of MRI and endoscopic video feeds from edge devices to the cloud. • Develop scalable Edge-to-Cloud AI solutions, ensuring low-latency inference for various medical applications. • Implement AI models that analyze video content and classify frames as useful or non-useful. • Develop AI-driven video segmentation and classification models to filter relevant vs. non-relevant frames. • Develop object detection, segmentation, and tracking models to identify anatomical structures, surgical instruments, and procedural steps in real time. • Implement video enhancement and denoising techniques to improve image clarity and feature extraction. • Deploy deep learning-based models for medical video analytics using TensorFlow, PyTorch, and OpenCV. • Compare real-time footage with pre-trained medical video datasets to generate automated insights. • Develop containerized AI models (Docker, Kubernetes) to ensure scalable deployment in hospital environments. • Integrate AI-powered video analytics pipelines with cloud-based AI models (e.g., Azure AI) • Ensure seamless bi-directional communication between cloud AI models and edge computing systems. • Work closely with radiologists and healthcare professionals to fine-tune AI-driven video object detection and recommendations. • Integrate AI-powered video analytics solutions with existing hospital PACS, DICOM storage, and medical imaging infrastructure. • Ensure AI models comply with HIPAA, FDA, and medical device regulations for clinical deployment. Requirements Qualifications: • Demonstrated experience in computer vision, AI model development, and optimization. • Experience working with medical videos, including MRI, endoscopy, ultrasound, echo-cardiograms, and OCR-based recognition. • Proficiency in multi-modal 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.