Brandon Roy
Artificial Intelligence Developer at InChat
United States
Summary
🤩
Rockstar🎓
Top SchoolBrandon Roy is an Artificial Intelligence Developer with nine years of focused experience building production-ready AI systems and conversational agents. He currently leads InChat, a project that bridges OpenAI's ChatGPT with WhatsApp to deliver practical chatbot experiences and real-world user interaction. His background spans roles at OpenAI, BlackBerry, ServiceNow, and Adobe, giving him deep expertise in ML engineering, backend systems, and long-lived product development. An active contributor to open-source tooling, he refactored and packaged the MineRL environment to improve data pipelines and make the project PyPI-installable, highlighting a pragmatic focus on reproducibility and deployment. Brandon combines research-grade AI understanding with hands-on systems work, preferring solutions that streamline data ingestion and scale in production. Based in the United States and holding a Computer Science degree from the University of Florida, he blends legacy enterprise experience with modern conversational AI innovation.
9 years of coding experience
23 years of employment as a software developer
Bachelor's degree, Computer Science 2:1, Bachelor's degree, Computer Science 2:1 at University of Florida
Github Skills (10)
python10
data-pipeline9
python-multiprocessing8
multiprocessing8
multi-process8
apidoc7
api7
github-ci6
githubaction-workflow6
reinforcement-learning5
Programming languages (7)
Github contributions (5)
minerllabs/minerl
Apr 2019 - Dec 2021
MineRL Competition for Sample Efficient Reinforcement Learning - Python Package
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 releases, 213 reviews, 361 commits in 2 years 8 months
Contributions summary:Brandon primarily focused on refactoring and packaging the MineRL environment and data loading module. They modified the project's setup.py file to create a PyPI package and adjusted various module imports to align with the new package structure. The user also worked on implementing a data pipeline, adding code to load data and create a processing pool for batch iteration. Furthermore, they contributed code for environment and dataset downloads.
pythonminerlreinforcement-learningminerl-competitionlearning-python
brandonhoughton/intelijSettings
Sep 2018 - Mar 2021
Contributions:72 pushes, 1 branch in 2 years 5 months
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