Mekaeel Malik

Hey! I'm Mekaeel, a software engineer from Toronto and a Computer Science student at McMaster University.

I've worked across backend, data, and cloud engineering through internships at IBM and TD Bank Canada, and I'm currently an undergraduate research assistant building ML models to predict NBA player injury risk.

Outside of work, you can find me on the court, tinkering with a side project, or reading up on whatever CS rabbit hole I fell into that week. I'd love to connect, so feel free to reach out about anything!

Experience

IBM Incoming Data Services Developer Intern

Toronto, ON

Sept 2026 – Dec 2026

Building Hybrid Cloud and AI solutions for enterprise clients, developing containerized services with Docker and Kubernetes using Python and SQL.

Docker Kubernetes Python SQL

TD Bank Canada Software Engineer Intern / Coop

Toronto, ON

May 2026 – Aug 2026

Extending Databricks reporting pipelines on Azure to automate data ingestion and deliver hourly fraud analytics reports, powering the fraud team's real-time investigation workflows.
Analyzing diverse data sources to surface high-value use cases and quantify business impact for the fraud team's roadmap.

Databricks Azure Data Pipelines Fraud Analytics

TD Bank Canada Software Engineer Intern / Coop

Hybrid / Toronto, ON

May 2025 – Aug 2025

Delivered a scalable solution for on-demand event processing, addressing high-volume authentication needs (100,000+ monthly requests) by developing and deploying a RESTful API using Spring Framework.
Realized a 50% reduction in API load time via pre-caching with Spring Data, and cut database size by 10%+ and sub-calls by 60% through database normalization and an API adapter.

Spring Framework Spring Data REST APIs DB Normalization

McMaster University Undergraduate Research Assistant

Hamilton, ON

Aug 2025 – Present

Designing ML models to predict NBA player injury probability over a rolling forecast window using prior-window stats, supporting roster decision tools and predictive dashboards.
Assembled data pipelines integrating 5+ sources with Python (Pandas, Beautiful Soup, Selenium) and SQL, implementing ETL workflows to clean, validate, and structure millions of entries.
Engineered 10+ predictive features with SHAP analysis and built scikit-learn and XGBoost ML pipelines to train and evaluate models, contributing to a forthcoming research publication.

Pandas Selenium SQL SHAP scikit-learn XGBoost

Technical Skills

Languages Python Java C++ C SQL JavaScript TypeScript HTML/CSS

Tools Git GitHub Actions Azure Databricks CMake JIRA Jupyter Notebook

Libraries PyTorch TensorFlow scikit-learn XGBoost NumPy Pandas Selenium Spring Framework React

A Few Projects

Violina

DeltaHacks 2026

Real-time ML posture tracker for violinists — analyzes wrist positioning, arm angles, and joint alignment from webcam input.

Python MediaPipe React

View Project

MNIST Classifier

From-scratch C++

A neural network built from scratch in C++ with hand-written AVX2 SIMD kernels, hitting 98.2% test accuracy.

C++17 AVX2 CMake

View Project

View all projects →