AI/ML Engineer - Medical Imaging

AI/ML Engineer - Medical Imaging

Position Overview

We are seeking an accomplished AI/ML Architect with a strong background in medical imaging or computer vision, and proven experience in designing, leading, and deploying production-grade AI/ML applications. The ideal candidate has deep expertise in deep learning architectures, edge deployment, and model optimization, combined with leadership experience in guiding teams of AI/ML engineers and data scientists from research to deployment.

Key Responsibilities

• Architect and lead the design and deployment of AI/ML-based imaging solutions, from concept to production, ensuring scalability, robustness, and clinical or industrial compliance.
• Design and optimize deep learning models for computer vision and 3D imaging tasks, leveraging architectures such as U-Net, 3D U-Net, GANs, Attention Networks, and Transformers.
• Implement and optimize deep learning networks using ONNX, PyTorch, or TensorFlow, ensuring efficient model conversion, quantization, and edge deployment.
• Guide AI/ML engineering team, providing mentorship, technical guidance, and strategic direction for project execution.
• Oversee data annotation pipelines and ensure dataset integrity, quality control, and annotation accuracy in medical or vision-based datasets.
• Collaborate cross-functionally with product teams, clinicians, and software engineers to integrate AI models into production environments.
• Apply statistical methods and data-driven validation for performance evaluation and continuous improvement of deployed models.
• Develop and maintain best practices for MLOps, model versioning, and CI/CD for machine learning applications.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field (PhD preferred).
• 5+years of overall experience in software or AI engineering, with at least 5 years in AI/ML architecture and Industrial Applications
• Strong experience in computer vision applications across domains such as imaging, object detection, segmentation, and 3D vision.
• Proficiency in Python, ONNX Runtime, and deep learning frameworks (PyTorch, TensorFlow, Keras).
• Proven experience deploying production-grade AI/ML solutions, including edge and cloud deployment.
• Expertise in deep learning model optimization, including pruning, quantization, and mixed-precision techniques.
• Familiarity with GANs, U-Net, 3D CNNs, Attention/Transformer-based networks, and autoencoders.
• Hands-on experience with data annotation, dataset management, and statistical analysis of model performance.
• Strong understanding of AI pipelines, MLOps, and DevOps practices for model lifecycle management.
• Excellent communication and leadership skills with the ability to manage and mentor multi-disciplinary teams.

Preferred Skills

• Exposure to edge AI frameworks (NVIDIA TensorRT, OpenVINO, CoreML).
• Knowledge of containerization (Docker, Kubernetes) and cloud services (AWS, Azure, GCP).
• Familiarity in applying statistical modeling and uncertainty estimation methods to evaluate and improve AI model performance.

Nice to have

• Experience in medical imaging AI (radiology, pathology, cardiology, or 3D reconstruction).

• Experience with regulatory-compliant AI systems (FDA/CE for medical applications).

• Experience in medical imaging (DICOM, radiology workflows) domain