AI Analytics Engineer

Company : Yd Talent Solutions

Job Type : Full Time

Santa Clara, United States

Number of Applicants

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000+

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Job Description - AI Analytics Engineer

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.

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