AI Engineer

AI Engineer – HealthcareAbout the roleThe Dubai Health Data & AI Lab is looking for an experienced AI Engineer to design, develop, validate, deploy and maintain AI-powered applications that improve clinical care, biomedical research and healthcare operations.You will work closely with clinicians, researchers, software engineers and data scientists to translate AI research into production-ready applications used in real-world healthcare settings.Key responsibilitiesAI & Machine LearningDesign, develop, train, validate and deploy machine learning and deep learning models using Python.Build reproducible AI pipelines using modern frameworks such as PyTorch, TensorFlow and scikit-learn.Develop applications using large language models (LLMs), multimodal foundation models, retrieval-augmented generation (RAG) and AI agents where appropriate.Evaluate model performance using appropriate validation, calibration, explainability and bias assessment techniques.Deploy AI models into production-ready research and clinical applications.Software EngineeringBuild web applications, dashboards, backend services and APIs that integrate AI models into clinical and research workflows.Design scalable, maintainable software architectures and reusable components.Develop secure, well-tested and production-ready software following software engineering best practices.Participate in code reviews, testing and continuous improvement.Data Engineering & Healthcare IntegrationWork with structured and unstructured healthcare datasets including: Electronic Health Records (EHR), Medical imaging (DICOM/NIfTI), Physiological and wearable time-series, Clinical text, Video, Laboratory, omics and biobank dataDevelop reliable data pipelines with appropriate validation, logging and traceability.Support healthcare interoperability standards including HL7, FHIR and DICOM.MLOps & DeploymentDeploy AI applications using Docker, FastAPI, MLflow, Kubernetes, Azure and CI/CD pipelines.Build and maintain model serving, experiment tracking and model versioning pipelines.Monitor production systems for performance, reliability, data drift and model degradation.Support deployment across on-premises and cloud environments.Security & GovernanceDevelop AI solutions that comply with Dubai Health information security, privacy and research governance requirements.Implement secure coding practices, authentication, authorization and audit logging.Support deployment within trusted research environments.CollaborationWork closely with clinicians, researchers, engineers and external technology partners.Translate clinical and operational requirements into practical AI solutions.Contribute technical documentation including API specifications, architecture diagrams, deployment guides and model documentation.Minimum qualificationsMaster's degree in Artificial Intelligence, Machine Learning, Data Science, Computer Science, Software Engineering or another quantitative discipline.Minimum 2 years of professional experience developing and deploying deep learning models using Python and modern deep learning frameworks (PyTorch and/or TensorFlow).Demonstrated experience building production-ready AI applications, including deployment through APIs, web applications or production services.Professional experience working with large unstructured datasets such as images, video, text, audio or time-series data.Strong software engineering skills including Git, SQL, Docker and CI/CD.Preferred qualificationsExperience with:healthcare, biomedical or other regulated datasets.medical imaging, EHR data, physiological signals, wearable data or multimodal AI.FastAPI, MLflow, Kubernetes and Azure.deploying and monitoring AI applications in production.developing clinician-facing or research-facing applications.Technical skillsPython, PyTorch and/or TensorFlow, scikit-learn, pandas and NumPy, SQL, REST APIs, FastAPI, Docker, MLflow, Kubernetes, Git & CI/CD, Linux, Azure (or equivalent cloud platform)What we're looking forWe're looking for someone who enjoys building complete AI solutions, not just training models.The ideal candidate is comfortable taking an idea from raw data through model development, application development, deployment and production support while working closely with clinicians and researchers.