SaaS · Artificial Intelligence · Full Stack
UmEmployed
UmEmployed matches candidates to jobs using a connected pipeline of AI models rather than isolated scripts: resume parsing, scoring, job matching, and recommendations feeding straight into the web dashboards and the mobile app.
Tech stack
Engineering highlights
My contributions
Designed and built the Django and Django REST Framework backend: API architecture, OAuth2-based authentication and authorization, and Stripe/PayPal payment integration.
Built React dashboard interfaces that surface the platform's AI outputs (resume scores, match results, recommendations) in a form recruiters and candidates could act on directly.
Built the cross-platform React Native app end to end and shipped it to both the Google Play Store and Apple App Store, including release management and production maintenance.
Built resume parsing, resume scoring, AI job matching, skill extraction, an AI recommendation system, MCQ interview-question generation, a Career Roadmap engine, and a Skills Guide engine.
Deployed on Azure and Docker, migrated to PostgreSQL with Alembic, and maintained CI/CD and infrastructure across AWS S3, RDS, Lambda, and IAM.
Deployed 6+ AI models at over 85% accuracy for résumé scoring, job matching, and personalized recommendations, in production use by 500+ users.