Michael Chaney

Led the design and deployment of generative AI and large language model systems in production, including scalable ML pipelines and real-time inference. Focused on model optimization for accuracy, latency, and reliability and mentored junior engineers.

Contributed to predictive analytics and recommendation systems for enterprise clients. Built scalable ML pipelines and applied MLOps practices such as CI/CD for ML models and automated testing to improve deployment speed and performance.

Developed and deployed ML models for classification, forecasting, personalization, and anomaly detection. Worked on NLP projects including sentiment analysis, text classification, and entity recognition, and integrated AI models into production applications.

Created a reusable React hook for scalable asynchronous connections using React Query. Worked with LoopBack and Node.js to update and manage the backend, including API endpoints and third-party integrations.

Built a rapid prototype process that set up authentication, backend data models, reusable front-end components, and async logic quickly. Developed a React Native voting application using GraphQL subscriptions for real-time voting and user feedback and managed native dependencies and async logic.

Developed backend systems, REST APIs, and scalable microservices for enterprise applications. Supported early AI initiatives with data preprocessing and integration work and participated in Agile full software development lifecycle activities.

Earned a Master of Science in Artificial Intelligence at The University of Texas at Dallas from 2013 to 2015.