Blake Sonnier, AI/ML Engineer

About

AI/ML Engineer with 7+ years of experience delivering data-driven solutions in healthcare and healthtech. Specializes in deploying deep learning models for diagnostic imaging, real-time patient monitoring, and risk prediction systems. Proven ability to bridge clinical insight with scalable AI systems using Python, PyTorch, and cloud-native technologies.

Experience

Invene

Feb 2022 – Present

McKinney, TX

Full Stack AI/ML Engineer

・Led the development of a real-time EEG stress/fatigue detection model deployed in ICU monitoring devices, achieving >90% precision and <200ms inference latency using quantized ONNX models. ・Built a deep learning segmentation pipeline for brain MRI scans using U-Net, reducing manual annotation time by radiologists at NeuroCare clinics. ・Engineered an edge deployment system for cognitive state models on embedded Linux ICU hardware using Docker and PyTorch optimizations. ・Developed clinician-facing dashboards using Plotly Dash to visualize EEG waveform states and segmentation masks for assisted diagnosis.

Axxess Technology Solutions

Oct 2020 – Jan 2022

Dallas, TX

AI/ML Engineer

・Built an NLP-based email triage system using fine-tuned BERT models to auto-classify support inquiries, saving 30+ hours/week in manual review. ・Developed a model explainability layer using SHAP and LIME for SMB loan default predictions, aiding compliance and analyst transparency. ・Deployed scalable ML pipelines on GCP with FastAPI and Docker, supporting continuous retraining to adapt to data drift in financial health analytics. ・Collaborated with operations and support teams to integrate AI tools into production workflows with minimal latency overhead.

Iodine Software

Jun 2018 – Sep 2020

Austin, TX

Software Engineer

・Built an ETA prediction engine using historical delivery and GPS data, reducing ETA error from 30min to <10min through hybrid ML + ruleset approach. ・Developed LSTM-based truck demand forecast model for regional logistics, improving fleet utilization by 18%. ・Designed a geospatial clustering system for route optimization, cutting dispatch planning time and fuel consumption using HDBSCAN and OpenCV. ・Integrated ML systems into Node.js-based dashboards and automated inference pipelines using AWS Lambda.

Education

University of Texas

Aug 2016 – May 2018

Master of Science, Computer Science

Lamar University

Aug 2012 – May 2016

Bachelor of Science, Computer Science

Blake Sonnier

AI/ML Engineer

Skills

Languages