About Me
My background, expertise, and professional journey
Who I Am
I'm Blake Sonnier, a Full Stack Engineer with 7+ years of experience building scalable, production-grade applications in healthcare and cloud-based environments. I specialize in developing end-to-end solutions that bridge robust backend services with intuitive frontend experiences.
My expertise spans modern web technologies and cloud platforms, with deep proficiency in Python, TypeScript, FastAPI, React, and cloud-native tools across AWS, GCP, and Azure. I've contributed to various AI/ML projects involving clinical decision support, documentation automation, and data pipelines.
As a Full Stack Engineer at Invene, I focus on architecting and delivering comprehensive healthcare applications. I combine strong software engineering principles with practical ML/AI knowledge, particularly in areas like LLMs and NLP, to create solutions that make healthcare data more accessible and actionable.
Based in Lumberton, Texas, I'm always exploring new opportunities to apply my technical expertise to solve meaningful healthcare challenges.
Education
Master of Science in Computer Science
The University of Texas at Austin
Focused on Artificial Intelligence and Machine Learning. Capstone: Diabetic retinopathy detection using CNNs on retinal images. Courses included ML, DL, NLP, and Reinforcement Learning with Prof. Peter Stone. Graduate Research Fellow in AI for Population Health.
Bachelor of Science in Computer Science
Lamar University
Dean's List (4 semesters). Participated in ACM Student Chapter, worked as Data Mining Lab Assistant, and was a Hackathon Finalist. Final-year project focused on building a recommendation system using collaborative filtering.
Key Skills
Languages
ML/Data Science
Frameworks/Libraries
Cloud/DevOps
Healthcare Tech
Software Engineering
Professional Strengths
- •Deep expertise in deep learning model optimization for healthcare applications, including quantization, pruning, and model compression
- •Experience with the complete machine learning lifecycle from data preparation to production deployment
- •Ability to bridge technical and clinical domains, translating healthcare needs into effective AI solutions
- •Skilled in developing interpretable AI solutions with explainable outputs for clinical and regulatory requirements
- •Strong communication skills with experience presenting complex technical concepts to diverse stakeholders