Overview

The Waterloo Experience Accelerate (WEA) Program – Microsoft AI is a joint internship initiative between the University of Waterloo and Microsoft Canada.

Interns receive workplace readiness training plus Azure Cloud AI/ML training, and then work in teams on a real-world project sponsored by Microsoft.

In this program, I joined as a Python full-stack developer intern, focusing on data collection, backend architecture, and applied machine learning on Azure.

Project – Detecting Bias in Job Postings

Our team built a web AI solution that identifies biases in tens of thousands of job postings.

• Designed and implemented a Python backend API to ingest, process, and serve job posting data.
• Created a data scraping and collection pipeline to gather large volumes of postings for analysis and model training.
• Used Azure Machine Learning to train custom models to detect potential bias in job descriptions and requirements.
• Deployed the solution using Azure Cloud services so it could scale to tens of thousands of records.

Role & Collaboration

• Acted as the technical lead on a team of 4.
• Helped teammates who had limited software engineering experience to learn and work with Azure Cloud and AI/ML tooling.
• Reviewed code, guided architecture decisions, and ensured the end-to-end pipeline—from data collection to model inference—was robust and maintainable.

Technical Highlights

• Python Full-Stack Development: Implemented backend services and integrated them with a web front end.
• Data Architecture & Collection: Designed the data scraping architecture and pipelines for large-scale job posting collection.
• Custom AI Models: Engineered preprocessing, feature extraction, and model training workflows to identify biased language in text.
• Azure Integration: Leveraged Azure services (compute, storage, and ML) to run experiments and host the solution.

Outcome & Performance

- Successfully delivered an AI-powered system capable of scanning and analyzing **tens of thousands of job postings** for potential bias.
- **Received an excellent performance rating** from the employer for technical contributions, leadership, and mentoring within the team.