What are the responsibilities and job description for the Computer Vision Analytics Engineer – Medical Video/Image Analytics position at Themesoft Inc.?
Job Title: Computer Vision Analytics Engineer – Medical Video/Image Analytics
Location: SFO/Santa Clara, CA – Onsite – 3-4 days in a week in the office.
Duration: 12 Months ( Contract to Hire)
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.
Qualifications:
- 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.
Regards,
Vignesh
vignesh.k@themesoft.com
