Subject Matter Expert

Description :We are seeking an accomplished Subject Matter Expert (SME) Medical Imaging AI to lead the design, validation, and deployment of advanced AI solutions in the medical imaging domain.This role blends deep domain expertise in medical imaging modalities (DICOM/HL7) with advanced technical proficiency in AI/ML frameworks such as PyTorch and TensorFlow.The ideal candidate will possess a strong background in computer vision, convolutional neural networks (CNNs), and healthcare data compliance (HIPAA, HL7, FDA standards).You will collaborate with AI research scientists, radiologists, and product teams to architect and operationalize imaging intelligence systems that enhance diagnostic accuracy, workflow efficiency, and clinical decision support.What Youll Do :- Lead end-to-end development of AI-driven medical imaging models focused on classification, segmentation, and anomaly detection.- Architect and validate deep learning pipelines using CNNs, transformer-based architectures, and multi modal data fusion.- Work with DICOM and HL7 standards for image acquisition, metadata processing, and interoperability across PACS/RIS systems.- Collaborate with radiologists and clinical experts to define annotation protocols, evaluate AI outputs, and ensure diagnostic relevance.- Integrate imaging AI solutions with healthcare platforms and cloud-based AI infrastructure for scalable deployment.- Ensure regulatory and compliance adherence including HIPAA, FDA, and CE certification standards.- Contribute to dataset curation, quality assurance, and augmentation strategies for model robustness.- Conduct model performance benchmarking using clinical-grade metrics such as sensitivity, specificity, and ROC-AUC.- Lead cross-functional collaborations with data scientists, MLOps engineers, and clinical informatics teams to accelerate product readiness.- Participate in research, publishing, and internal knowledge-sharing initiatives to advance the organizations AI maturity in healthcare.What You Bring :- 10+ years of professional experience in AI for medical imaging, computer vision, or clinical informatics.- Deep expertise in DICOM, HL7, and medical imaging data standards.- Hands-on experience building and deploying CNN and transformer-based models for radiology applications.- Proficiency in Python, TensorFlow, and PyTorch for model development and optimization.- Strong understanding of radiology workflows, imaging modalities (CT, MRI, X-ray, Ultrasound), and related metadata structures.- Knowledge of image preprocessing, segmentation techniques, and annotation frameworks.- Practical exposure to cloud AI environments (AWS HealthLake, Azure AI for Health, GCP Healthcare API) and MLOps pipelines.- Experience working with clinical datasets, de-identification pipelines, and maintaining compliance with healthcare data governance.- Strong analytical and communication skills to bridge the gap between AI engineering and clinical practice.- Masters or Ph.D. in Biomedical Engineering, Computer Science, Medical Imaging, or a related field.Preferred Skills :- Experience in multi-modal AI combining imaging, text (radiology reports), and EHR data.- Familiarity with 3D imaging techniques, volumetric CNNs, or GAN-based synthetic data generation.- Prior involvement in FDA 510(k) or CE marking processes for AI-based medical software.- Research publications or contributions in peer-reviewed medical AI journals or conferences.- Hands-on experience with AI explainability, bias detection, and model interpretability for clinical deployment.Why Join Us :This is a rare opportunity to be at the forefront of AI-driven medical innovation, contributing to solutions that directly impact patient outcomes and healthcare efficiency.As a Medical Imaging AI SME, youll collaborate with leading researchers, clinicians, and engineers to translate cutting-edge AI into real-world diagnostic support systems.The environment fosters scientific rigor, clinical relevance, and ethical AI practices offering you the platform to shape next-generation medical imaging solutions that bridge the gap between research and clinical application (ref:hirist.tech)