Computer Vision Engineer- Medical Imaging

Educational Requirements

• Bachelor of Engineering
• Bachelor of Technology
• Bachelor of Science
• Bachelor of Comp. Applications
• Master of Engineering
• Master of Technology
• Master of Comp. Applications
• Master of Science

Service Line Responsibilities

• Design, develop, and optimize AI/ML algorithms for medical image analysis, segmentation, and 3D reconstruction from TEE and CT images.
• Research and implement advanced deep learning architectures including CNNs, GANs, VAEs, and Diffusion Models for medical imaging tasks.
• Develop robust 3D reconstruction pipelines from 2D image data and multi-view geometries, tailored to medical imaging workflows.
• Perform multimodal image registration (CT-CT, CT-MRI, Fluro-Endo, 2D-3D) and develop tools for alignment, calibration, and fusion.
• Enhance and denoise medical images using advanced computer vision and AI-based enhancement techniques.
• Work extensively with DICOM data, integrating with PACS systems for data ingestion and retrieval.
• Collaborate with teams for dataset curation, labeling, and ground truth generation.
• Develop scalable training and inference pipelines on cloud platforms (AWS preferred; Azure/GCP acceptable).
• Ensure reproducibility and traceability in experiments using MLOps practices (Docker, MLflow, or similar).
• Collaborate with software engineers to integrate AI components into production-grade imaging applications.
• Document research findings, maintain version-controlled repositories, and contribute to technical publications or IP filings.
• Stay up-to-date with emerging trends in AI, computer vision, and medical imaging technologies.

Technical and Professional Requirements

• Experience in 2D and 3D medical imaging (CT, MRI, Ultrasound, TEE) and DICOM data handling.
• Strong understanding of 3D geometry, camera calibration, stereo vision, and multi-view reconstruction.
• Experience in segmentation, registration, and object tracking within medical image contexts.
• Proficiency with classical computer vision techniques (OpenCV, PCL, feature detection, structure-from-motion, SLAM, etc.).
• Knowledge of generative and reconstruction models (GANs, VAEs, Diffusion Models) and fine-tuning methods for domain-specific applications.

Preferred Skills

• Image Video processing
• Generative AI