Backend · AI/ML Systems · Distributed Systems
I'm a Computer Science graduate from the University of Massachusetts Amherst with academic and project experience in backend engineering, distributed systems, machine learning, reinforcement learning, and artificial intelligence. Currently seeking software engineering opportunities involving backend development and AI/ML-related technologies in collaborative and technically driven environments.
Engineering areas that interest me:
I enjoy exploring how intelligent systems learn and make decisions through reinforcement learning and machine learning. Building environments, experimenting with different agents, and analyzing their behavior is one of the areas of computer science that interests me the most.
I like building backend systems because they power the core functionality behind applications and services. Designing APIs, handling real-time communication, and managing data flow are problems I find both challenging and rewarding.
I am interested in how distributed systems coordinate and communicate under concurrent workloads and failures. Learning about synchronization, replication, and fault tolerance has given me a deeper appreciation for reliable system design.
Selected systems work across reinforcement learning, distributed simulation, and production-style backends.
A reinforcement learning framework built for experimenting with PPO, DQN, and A2C agents in custom Gymnasium environments. Includes configurable training workflows, evaluation tooling, and reproducible testing pipelines for comparing agent performance.
A multi-agent poker AI project focused on strategic decision-making under uncertainty through simulation, opponent behavior modeling, and self-play training. The project explores how agents adapt their strategies in adversarial environments with incomplete information.
A distributed systems simulation exploring concurrency, coordination, replication, and fault tolerance through a socket-based marketplace environment with buyers, sellers, traders, and warehouse services.
A real-time office hour queue management system designed to support live queue updates, session management, and communication between students and faculty through backend APIs and WebSocket synchronization.
A full-stack AI agent platform using LangGraph, FastAPI, Next.js, and GPT-4o that automates resume optimization and job application workflows — from match scoring to cover letter generation and interview prep.
Languages, platforms, and systems concepts used across backend, ML, and distributed work.
Python
Java
C
C++
JavaScript
TypeScript
PyTorch
Docker
PostgreSQL
MongoDB
Express.js
FastAPI
Git