Param Teraiya

- Shipped ML-based Log Clustering, improving RCA speed by 60%, reducing incident resolution from hours to minutes for large-scale cloud environments.
- Built Model Context Protocol (MCP) server that streams logs and traces to LLMs, giving every debugging prompt real-time observability context.
- Created a Root Cause Analysis AI Agent that pinpoints failure sources and proposes code/config fixes.

Built a python data pipeline & schedulers framework that syncs third party sources with BloomChat AI. Improved data quality and reliability by implementing Pydantic validation in FastAPI endpoints, preventing schema drift and ensuring smooth handoff between microservices and downstream data pipelines.

Led a Client-facing team to ship feature updates, manage change requests, and resolve production issues. Built backend APIs and deployment pipelines for computer vision systems, handling live streaming data at scale.

Built and deployed furniture recognition models using deep learning on AWS. This involved developing and implementing machine learning algorithms for image recognition.

Developed Hyperspectral Image Reconstruction from the RGB algorithms. This project focused on advanced image processing techniques.

Integrated sensor-based data pipelines for real-time process monitoring in manufacturing. Developed permeability and viscosity tests using IoT sensors and microcontrollers.

Grade: 8.07/10

Majored in Information & Communication Technology (ICT) with a CGPA of 8.07/10. The curriculum focused on a broad range of topics within the ICT field.