Blake Sonnier

I am a Full Stack AI/ML Engineer with over 7 years of experience in delivering innovative, data-driven solutions within the healthcare and healthtech sectors. My expertise lies in deploying deep learning models for diagnostic imaging, real-time patient monitoring, and risk prediction systems. I excel at bridging clinical insights with scalable AI systems, utilizing technologies such as Python, PyTorch, and cloud-native solutions.

In my current role at Invene, I led the development of a real-time EEG stress and fatigue detection model, achieving over 90% precision and under 200ms inference latency. I have also built a deep learning segmentation pipeline for brain MRI scans, significantly reducing manual annotation time for radiologists. My work includes engineering edge deployment systems for cognitive state models and developing clinician-facing dashboards that enhance diagnostic capabilities.

Previously, I contributed to Axxess Technology Solutions by creating an NLP-based email triage system that saved over 30 hours a week in manual review. I have a strong background in deploying scalable ML pipelines and integrating AI tools into production workflows, ensuring minimal latency overhead. My journey in software engineering began at Iodine Software, where I developed predictive models that improved logistics and fleet utilization.