J.D. Fagan

J.D. Fagan is a highly skilled and versatile professional with a passion for coding, problem-solving, and innovation in the field of AI, particularly in machine learning and deep learning applications.

With a background in Computer Science from Rochester Institute of Technology, continuing education in Business from Harvard University, and Statistics and Machine Learning from Stanford University, J.D. brings a wealth of knowledge and expertise to his roles.

Having held positions ranging from a Software Engineer Intern at IBM to a Staff Data Engineer at Doma and Staff Data Engineer at Claritas Rx, J.D. has honed his skills in data engineering, software architecture, and machine learning.

J.D. excels in building ELT data pipelines, designing and implementing cloud resources on AWS and Azure, and automating infrastructure using tools like Terraform and Terragrunt.

He is proficient in writing numerical analysis code in Python, Matlab/Octave, and SQL, and is experienced in operational processes such as Git, CI/CD, and IaC deployment tasks.

Furthermore, J.D. has a keen interest in working with Deep Learning Neural Nets for vision-based and NLP applications, deploying and maintaining deep learning models like LLMs, and exploring new possibilities in AI workflows.

His extensive experience in roles like Data/Software Architect, Lead Data Engineer, and Senior Software Engineer at companies such as Waypoint, DHL, and Wells Fargo showcases his adaptability and expertise in diverse technical environments.

Known for his ability to learn new technologies quickly and his hands-on approach to problem-solving, J.D. is a valuable asset for businesses looking to tackle complex data challenges, build innovative AI solutions, and streamline their tech processes.

His passion for collaborative innovation, technical architecture, and mentoring make him a well-rounded professional who can contribute effectively to a wide range of projects and teams.