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I’m a Bachelor of Computer Science student at Concordia University in Montréal, passionate about building reliable software and turning ideas into working products. I enjoy designing systems with solid fundamentals—whether that’s creating a turn-based strategy game engine, building full-stack web applications, or developing data-driven features that feel fast and intuitive for users.…

Rahath Ahmed Montréal

Game Developer, Programmer, Full Stack Developer, +3

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I’m a Bachelor of Computer Science student at Concordia University in Montréal, passionate about building reliable software and turning ideas into working products. I enjoy designing systems with solid fundamentals—whether that’s creating a turn-based strategy game engine, building full-stack web applications, or developing data-driven features that feel fast and intuitive for users.…

Available to hire

I’m a Bachelor of Computer Science student at Concordia University in Montréal, passionate about building reliable software and turning ideas into working products. I enjoy designing systems with solid fundamentals—whether that’s creating a turn-based strategy game engine, building full-stack web applications, or developing data-driven features that feel fast and intuitive for users.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Beginner

Language

English

Fluent

French

Fluent

Work Experience

SmartCart Recommendation System (Project) at Project build

February 1, 2026 - March 1, 2026

Built a user-based collaborative filtering recommendation engine in Python using cosine similarity and tuned top-5 neighbor scoring on sparse e-commerce data. Applied Apriori association rule mining to discover frequent itemsets and product association rules, identifying cross-sell patterns with strong confidence and lift. Developed an end-to-end SmartCart notebook pipeline with preprocessing, evaluation, and visualization for a dataset with high sparsity, producing user similarity heatmaps and top-5 group recommendation insights including category diversity.

SmartCart Recommendation System Developer

February 1, 2026 - March 1, 2026

Built a user-based collaborative filtering recommender in Python using cosine similarity and tuned top-5 neighbor scoring on sparse e-commerce data. Applied Apriori association rule mining to discover frequent itemsets and cross-sell rules with strong confidence and lift. Produced an end-to-end notebook pipeline with preprocessing, evaluation, and visualization for a 50-user x 100-product dataset, generating user similarity heatmaps and diversified top-5 group recommendation insights.

Student Course Tracker (Project) at Project build

January 1, 2026 - February 1, 2026

Created a full-stack React/Vite + Node.js/Express CRUD student course tracker covering end-to-end workflows: adding students, adding courses, enrolling students, assigning grades, and viewing records by course or student. Designed and connected 10 frontend actions to 10 Express routes for real-time management of students, courses, enrollments, and grades. Modeled a normalized MySQL schema with three linked tables, foreign keys, cascade deletes, and uniqueness constraints to preserve enrollment integrity.

Student Course Tracker Developer

January 1, 2026 - February 28, 2026

Built a full-stack CRUD student course tracker using React/Vite and Node.js/Express covering core workflows: adding students, adding courses, enrolling students, assigning grades, and viewing records by course or student. Designed and connected 10 frontend API actions to Express routes to enable real-time management of students, courses, enrollments, and grades. Modeled a normalized MySQL schema with linked tables, foreign-key constraints, cascade deletes, and uniqueness rules to preserve enrollment integrity.

AI Meeting Bot (Project) at Project build

December 1, 2025 - January 31, 2026

Developed an AI-powered meeting assistant that can join Zoom, Google Meet, and Microsoft Teams calls via the Recall.ai Meeting Bot API, streaming live meeting data into a React/Vite frontend. Built participation analytics such as live transcription, participant join/leave tracking, speaker activity monitoring, and visualizations for word-share/time-share and conversation balance (interruptions, silence, and turn-taking). Integrated the OpenAI Responses API to produce on-demand participation coaching and structured post-meeting summaries (decisions, action items, and inclusivity insights) using an Express backend and webhook-based event handling.

AI Meeting Bot Developer

December 1, 2025 - January 31, 2026

Developed an AI-powered meeting assistant that can join Zoom/Google Meet/Microsoft Teams using the Recall.ai Meeting Bot API and stream live meeting data into a React/Vite frontend. Created real-time participation analytics such as live transcription, participant join/leave tracking, speaker activity monitoring, word/time visualizations, and conversation balance metrics (interruptions, silence, and turn-taking). Integrated the OpenAI Responses API for on-demand participation coaching and structured post-meeting summaries (decisions, action items, inclusivity insights) using an Express backend and webhook-based event handling.

Modular Risk Game Engine (Project) at Concordia University (Project)

September 1, 2025 - October 1, 2025

Built a Risk-style modular C++ game engine using OOP principles, including players, maps, cards, orders, game states, and turn-based gameplay phases. Implemented map loading and validation, plus console/file command processing with an adapter-based command approach. Developed a state-driven startup flow for loading maps, adding players, and beginning games. Applied design patterns such as Observer-based logging and Strategy-based AI behaviors, including aggressive, benevolent, neutral, and cheater players.

Modular C++ Game Engine Developer

September 1, 2025 - October 1, 2025

Built a Risk-style modular strategy game using OOP principles, including players, maps, cards, orders, game states, and turn-based gameplay phases. Implemented map loading and validation, plus console/file command processing with a state-driven startup flow for loading maps, adding players, and starting games. Applied design patterns (Observer logging, Adapter-based file commands, Strategy-based AI behaviors) to support different AI types including aggressive, benevolent, neutral, and cheater players.

Private Community Social Network (Project) at Project build

December 1, 2024 - January 31, 2025

Built a secure two-tier LAMP-stack web application enabling members to create profiles, share posts, and interact via an integrated messaging system. Added scalable group management capabilities such as member addition/removal and permission settings, supporting collaboration among 50+ simulated users. Enhanced performance by optimizing SQL queries and secured transactions with prepared statements and input validation, improving query execution time by about 30%.

Private Community Social Network Developer

December 1, 2024 - January 31, 2025

Developed a secure two-tier web application with the LAMP stack enabling members to create profiles, share posts, and communicate through an integrated messaging system. Implemented group management features including member add/remove and permission settings, supporting collaboration among 50+ simulated users. Optimized database performance by improving SQL queries and secured transactions using prepared statements and input validation, resulting in a measured ~30% improvement in query execution time.

AI Image Classification (CIFAR-10) (Project) at Project build

November 1, 2024 - November 30, 2024

Implemented and trained multiple image classification models from scratch on CIFAR-10, including Naive Bayes, Decision Tree, Multi-Layer Perceptron, and CNN models using Python with PyTorch, plus Scikit-learn. Evaluated using confusion matrices and calculated precision, recall, and F1-score, along with data visualizations using Pandas. Improved generalization by reducing overfitting through hyperparameter tuning and regularization, achieving test accuracy improvements over baseline models.

AI Image Classification (CIFAR-10) Developer

November 1, 2024 - November 30, 2024

Implemented and trained multiple image classification models from scratch, including Naive Bayes, Decision Tree, Multi-Layer Perceptron, and CNN variants on the CIFAR-10 dataset using Python with PyTorch and scikit-learn. Evaluated models using confusion matrices and computed precision, recall, and F1-score; produced visualizations using Pandas. Improved generalization by reducing overfitting through hyperparameter tuning and regularization, achieving test performance improvements relative to baselines.