Bryan Maione

Senior Full Stack Engineer and AI Engineer with 10+ years of experience developing enterprise-scale full-stack web applications, leading engineering teams, and building AI-powered products end-to-end. Led development of Generative AI–enabled applications, copilots, LLM-integrated workflows, and AI search experiences supporting millions of users. Deep experience integrating AI safely into scalable web apps, designing clean APIs, and implementing CI/CD, observability, accessibility, and performance best practices.

Bryan Maione is a Principal Machine Learning Engineer and Senior Full Stack Engineer with 10+ years of hands-on experience building AI-powered products, enterprise web applications, and high-performance data systems. He has led end-to-end development of Generative AI solutions—including copilots, agentic systems, LLM-driven analytics, and RAG pipelines—used across healthcare, medical devices, defense research, and imaging platforms.

He has architected large-scale AI/ML platforms that process millions of records, designed production-grade deep learning models, and optimized GPU inference pipelines using PyTorch, ONNX Runtime, CUDA, and TensorRT. Bryan builds secure, compliant, cloud-native systems across AWS, GCP, and Azure, and has delivered highly reliable MLOps pipelines using Kubeflow, Vertex AI, SageMaker, Docker, and Terraform.

A strong full-stack engineer, he develops robust Angular/React front-ends and Python/Node back-ends, building real-time dashboards, APIs, and mission-critical tools with an emphasis on performance, accessibility, and long-term maintainability. His work spans clinical analytics platforms, surgical imaging systems, ISR/EO/IR research tools, and enterprise-grade AI copilots.

Across his roles, he has consistently led teams, improved system reliability, created scalable architectures, and delivered compliant AI systems for healthcare, defense, and high-performance imaging domains.

Bryan holds a PhD in Artificial Intelligence from North Carolina State University, supported by prior graduate work in Electrical and Electronics Engineering at both the master’s and doctoral levels. His academic foundation includes advanced coursework and research in machine learning, computer vision, signal processing, and high-performance imaging systems. He further strengthened his AI expertise through Udacity’s AI for Healthcare Nanodegree, focusing on applied machine learning, medical-AI workflows, and production-ready model development.