Artificial Intelligence Engineer I

AI/ML Model Development & Deployment

• Collaborate closely with clinical researchers, physicians, and engineering teams to design, build, and deploy machine learning models for healthcare applications.
• Assist in the development of AI-powered solutions spanning genomics analysis, EHR data mining, medical imaging interpretation, and diagnostic support systems.
• Support robust ML pipelines from research prototypes to production-ready systems in clinical environments.
• Ensure models meet healthcare regulatory standards and maintain high performance in real-world clinical settings.

Technical Implementation

• Assist in the design and implement scalable AI/ML architectures using modern frameworks (TensorFlow, PyTorch, scikit-learn).
• Documents data pipelines for processing complex clinical data from sources like Epic Clarity, REDCap, omics platforms, and medical imaging systems.
• Develop and maintain infrastructure for integrating multi-modal healthcare data (genomics, transcriptomics, proteomics, imaging, EHR).
• Optimize model performance for deployment on various platforms including cloud infrastructure and edge devices.
• Integrate AI models with existing healthcare IT systems and electronic health records.

Research & Innovation

• Stay current with latest developments in healthcare AI, including applications in precision medicine, computer vision, and natural language processing.
• Collaborate on research publications and presentations at medical and technical conferences.
• Participate in grant proposals and collaborative research initiatives with clinical departments.
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We are seeking an innovative AI Engineer I to join our team at the Baylor College of Medicine and Texas Children’s Hospital Data Science Center, where cutting-edge artificial intelligence meets healthcare delivery. This position offers a unique opportunity to collaborate and deploy AI/ML models that directly impact patient care and clinical workflows. You’ll work at the intersection of multi-omics data, electronic health records, medical imaging, and clinical decision support systems., * Bachelor’s degree in Computer Sciences, Statistics, Mathematics, or a related field or four years of relevant experience may substitute for degree requirement.

• No prior experience required with Bachelor’s degree.

Department-specific Requirements

• Strong proficiency in Python programming with experience in ML/AI libraries (NumPy, pandas, scikit-learn, TensorFlow/PyTorch).
• Solid understanding of machine learning fundamentals including supervised/unsupervised learning, deep learning architectures, and model evaluation.
• Experience with ML model deployment and production systems.
• Familiarity with version control (Git) and collaborative development workflows.

Preferred Qualifications and Skills

• Background in genomics data processing, bioinformatics pipelines, or clinical informatics.
• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Experience working with large-scale omics datasets (RNA-seq, whole genome/exome sequencing, proteomics).
• Understanding of clinical workflows and electronic health record systems.
• Experience with medical imaging analysis or natural language processing for clinical text.