Principal AI Engineer

Sonic Healthcare USA, Inc., a national leader in laboratory and pathology services, is seeking a Principal AI Engineer to drive advanced analytics and AI solutions that support high-quality patient care. In this role, you will architect and deploy scalable machine learning systems leveraging clinical, laboratory, and imaging data to improve diagnostic accuracy, turnaround time, and operational efficiency. You will collaborate closely with clinicians, pathologists, and technical teams to translate medical needs into production-ready AI products, establish best practices for MLOps, and mentor data professionals. This hybrid role offers the opportunity to shape enterprise AI strategy within a patient-focused, quality-driven organization committed to innovation and continuous improvement.

Responsibilities

• Lead design and deployment of AI and machine learning solutions to enhance clinical and pathology workflows
• Collaborate with clinicians, laboratorians, and IT to translate diagnostic needs into scalable AI products
• Architect data pipelines using laboratory, imaging, and clinical data while ensuring data quality and governance
• Ensure AI models meet regulatory, privacy, and security requirements for healthcare data
• Mentor and guide data scientists and engineers, establishing best practices and coding standards
• Evaluate and integrate emerging AI technologies and tools into Sonic Healthcare's analytics ecosystem
• Monitor, validate, and continuously improve model performance in production environments
• Document architectures, models, and processes for clinical, technical, and compliance stakeholders

Required Skills

• Python
• Machine learning modeling (supervised, unsupervised, deep learning)
• MLOps and model deployment (Docker, Kubernetes, CI/CD)
• Cloud computing (AWS, Azure, or GCP)
• Data engineering (SQL, ETL, pipelines)
• NLP and/or computer vision techniques
• Software engineering best practices (Git, testing, code review)
• Big data frameworks (Spark or similar)
• Model monitoring and observability
• Knowledge of healthcare data standards (HL7, FHIR, DICOM)