Portfolio

About Me

Back-End Engineer & AI/ML Enthusiast | BTech CSE (AI/ML) at Christ University

Currently working as a Back-End Engineer Intern at GoQuant, where I architect multi-service systems using FastAPI, REST APIs, and WebSocket integrations. I specialize in building high-performance systems with Python and C++, working extensively with PostgreSQL, Redis caching, and low-latency market data pipelines that achieve 99.9%+ accuracy.

Previously at XenReality Technologies, I enhanced OCR accuracy by 15-20%, trained object detection models with 85% precision, and achieved 85% accuracy in real-time pose estimation while reducing GPU memory consumption by 25% through optimization techniques.

With expertise spanning Computer Vision, Deep Learning, and systems programming, I'm passionate about building scalable, efficient solutions. I'm also a game developer with strong skills in Unreal Engine, C++, and GPU programming, constantly pushing the boundaries of what's possible in both backend systems and interactive experiences.

Work Experience

Back-End Engineer Intern

GoQuant

Remote

June 2025 - Present

Architected multi-service support using FastAPI, REST API, and WebSocket integrations, establishing seamless communication between diverse system architectures.
Developed and maintained management systems in both Python and C++, ensuring robust cross-platform compatibility and efficient system integration.
Integrated multiple components and services into an existing middleware communication system that connects frontend applications with backend management systems, working extensively with PostgreSQL database operations and Redis caching in a Python environment.
Enhanced development efficiency with C++, GDB, and Linux, while leveraging Git and GitHub Actions to optimize workflows and boost productivity.
Engineered a low-latency market data pipeline processing high-volume message streams with exceptional accuracy above 99.9%, using modern C++ and concurrent multithreading to minimize data loss and maximize throughput.

AI & ML Software Engineering Intern

XenReality Technologies

Hybrid

August 2024 - May 2025

Improved OCR text recognition accuracy by 15-20% through advanced pre-processing techniques and algorithms, enhancing document processing capabilities and data extraction efficiency.
Trained object detection models achieving 85% precision and 80% recall rates for object classification and detection, enabling robust computer vision applications.
Enhanced data quality and model performance by 20% through implementation of anomaly detection algorithms and outlier detection techniques for improved reliability.
Achieved real-time pose estimation performance with 85% accuracy in tracking body movements and coordinates, enabling advanced motion analysis applications.
Reduced GPU memory consumption by 25% through model quantization and optimization techniques, improving cost-efficiency and scalability.