Equipment monitoring API
I work on a large platform that lets Caterpillar dealers explore machine telemetry and gives data scientists the tools to build rule-based and machine-learning models that identify potential equipment issues.
I helped rebuild the platform’s API in Go. The original implementation had inconsistent permissions, limited documentation, and several performance bottlenecks. I reworked authentication and authorization, centralized permission management, and added automatic OpenAPI generation so the API specification stays synchronized with the code.
I also redesigned pagination and worked with the UI team to adopt it throughout the application. This removed the need to build and regularly refresh large cached lists, reducing startup time from more than 30 seconds to about 1.5 seconds.
Model execution
On the execution side of the platform, I work closely with data scientists whose models run against equipment data. I gathered feedback about recurring failures and operational friction, then turned it into fixes that made model development and execution more reliable.
One of the larger changes was redesigning how custom Python environments—with packages such as NumPy and TensorFlow—are created and maintained. The new approach made environments easier to update, and I identified and removed roughly a dozen deprecated environments that had accumulated over time.