Systems programmer with a deep foundation in C/C++, algorithms and networking. Expanding into Data Science, Machine Learning and distributed systems. Built to solve hard problems.
Formed at 42 School — where the curriculum has no teachers, no textbooks and no shortcuts — I learned to engineer solutions through iteration, peer review and relentless debugging.
My foundation lives in systems programming: pointers, memory arenas, POSIX APIs, concurrency primitives. From there I built upwards into full-stack web, mobile and now Data Science and ML.
I approach every problem the same way — understand the machine, understand the math, write code that respects both.
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Raycasting Graphics Engine
A pseudo-3D raycasting engine inspired by Wolfenstein 3D, built from scratch in C. Implements full scene rendering, textured walls, a minimap system, player movement and mouse rotation — zero external graphics libraries beyond MiniLibX.
Multi-Client Real-Time Networking
A production-grade multi-client TCP chat server using raw POSIX sockets and I/O multiplexing via select(). Manages concurrent clients, broadcasts messages, and handles file descriptor lifecycle — no threads, no external libs.
Full-Stack Web Application
A modern fullstack web application built as a team project. Features JWT authentication, real-time client-server communication, containerised microservices and a polished Next.js frontend backed by a Spring Boot REST API.
Graph-Based Data Structures
An academic simulation of a social network modelled as an undirected weighted graph. Implements friend suggestion via BFS, mutual-friend detection, connected components analysis via DFS, and adjacency-list storage.
Cross-Platform Mobile
A mobile quiz application built with React Native. Features dynamic question banks, a points and streak system, animated feedback transitions and a leaderboard — deployed on both iOS and Android.
🏆 3rd Place — National · Hackathon
Competed in NASA Space Apps Challenge and achieved 3rd place nationally. Developed a data-driven solution combining open NASA datasets with machine learning techniques under a 48-hour hackathon sprint.
Looking for challenging engineering roles in Software Engineering, Data Science, ML/AI or Distributed Systems. Open to relocation and remote.