Computer Vision Engineer ( Biomedical Imaging)

Role Overview

As a Computer Vision Engineer, you will develop custom computer vision and image processing solutions for biomedical and healthcare applications. You will work with microscopy and medical imaging datasets, design advanced image analysis pipelines, and build deep learning models for segmentation, feature extraction, and modeling.

This role is ideal for candidates with strong computer vision fundamentals, hands-on OpenCV expertise, and an interest in solving challenging scientific problems.

Key Responsibilities

• Design and develop custom computer vision algorithms using Python, OpenCV, and deep learning frameworks.
• Build image analysis pipelines for biomedical imaging applications.
• Work with imaging datasets including Microscopy (Brightfield, Phase Contrast, Fluorescence), MRI, CT, and PET scans.
• Develop solutions for image segmentation, advanced segmentation, cell and tissue detection, feature extraction (featurization), image enhancement, denoising, and quantitative image analysis.
• Design and implement custom algorithms rather than relying solely on off-the-shelf software tools.
• Leverage and fine-tune modern deep learning and vision foundation models for biomedical imaging applications.
• Collaborate with scientists, AI researchers, and domain experts to solve real-world biomedical research challenges.
• Evaluate and optimize machine learning and deep learning models for performance and accuracy.

Qualifications, Skills & Candidate Profile

• B.E./B.Tech./M.Tech./M.S. in Computer Science, Artificial Intelligence, Computer Vision, Electronics, Electrical Engineering, Biomedical Engineering, or related disciplines.
• Freshers and experienced candidates (05 years) are welcome to apply.
• Strong programming skills in Python and hands-on experience with OpenCV.
• Good understanding of Computer Vision, Image Processing, Machine Learning, and Deep Learning.
• Experience implementing, training, and evaluating deep learning models using PyTorch or similar frameworks.
• Familiarity with modern vision architectures such as CNNs, Vision Transformers (ViTs), segmentation models, and vision foundation models.
• Knowledge of image segmentation, feature extraction, image enhancement, and custom image processing pipeline development.
• Exposure to biomedical imaging datasets such as Microscopy, MRI, CT, or PET images is preferred.
• Experience through industry projects, academic research, internships, thesis work, or personal projects.
• Familiarity with image annotation, model training, fine-tuning, and evaluation techniques.
• Strong analytical, problem-solving, and research skills.
• Prior experience in biomedical imaging or medical image analysis will be an added advantage.