Computer Vision Engineer – Medical Imaging

DATA SCIENTIST MEDICAL IMAGING & DIAGNOSTICS (AI / DEEP LEARNING)

ROLE OVERVIEW

We are building next-generation AI-powered medical diagnostic solutions focused on MRI, CT, and multimodal imaging. The role involves close collaboration with radiologists, clinicians, and regulatory teams to develop clinically robust, explainable, and production-ready AI models.

This position requires strong grounding in medical imaging, deep learning, andvalidationworkflows aligned with diagnostic and regulatory standards.

KEY RESPONSIBILITIES

Medical Imaging & AI Development

• Develop deep learning models for medical image classification, segmentation, detection, and quantitative analysis.
• Work extensively with MRI and CT DICOM datasets, including preprocessing, normalization, and artifact handling.
• Apply domain-aware image processing techniques informed by MRI/CT physics and pulse sequences.
• Design and train architectures such as CNNs, U-Net,ResNet, and related variants for diagnostic use cases.

Model Validation & Clinical Alignment

• Define and evaluate clinically relevant metrics (Dice score, sensitivity, specificity, ROC-AUC).
• Perform cross-dataset and cross-scanner validation to ensure robustness.
• Collaborate with radiologists and clinical experts to review model outputs and error cases.
• Implement explainability techniques such as activation maps and attention-based visualizations.

Data Engineering & Experimentation

• Curate, preprocess, and augment medical imaging datasets.
• Track experiments, datasets, and model versions usingMLflowor equivalent tools.
• Apply statistical methods to assess model performance, bias, and stability.

Deployment & Production

• Develop RESTful APIs using Flask or similar frameworks for model inference.
• Containerize and deploy models using Docker-based workflows.
• Integrate models into CI/CD pipelines with proper version control and testing.

Technical Skills

• Strongproficiencyin Python with hands-on experience usingPyTorch, TensorFlow, orKeras.
• Solid understanding of deep learning for computer vision and medical imaging.
• Experience handling DICOM images and medical imaging workflows.
• Familiarity with NumPy, OpenCV, Scikit-learn, Pandas.
• Experience building and deploying REST APIs.
• Working knowledge of MRI and CT physics, pulse sequences, and imaging artifacts.
• Experience applying AI to radiology use cases such as tumor detection, organ segmentation, or perfusion analysis.

Software & Engineering Practices

• Experience with Docker, Git-based version control, and CI/CD workflows.
• Understanding ofmodel testing, validation, and production readiness.

Education andExperience

• Bachelors orMasters degree in Biomedical Engineering, affinity towards medical image analysis, Medical Instrumentation, or related field.
• 24 years of relevant experience in medical imaging, AI, or medical device development.