Jinnalyst
AI-Powered Talent Augmentation Platform
Problem
Existing platform struggled with complex computer vision integrations, causing performance bottlenecks and code duplication across multiple assessment modules. Development velocity was slow due to lack of reusable component architecture.
Solution
Architected a scalable React.js frontend with modular component system using Material-UI and Redux Toolkit. Integrated face-api.js and Mediapipe through abstraction layers, enabling seamless computer vision features. Integrated Monaco Editor for technical coding assessments with custom syntax highlighting.
My Role
Frontend Team Lead / React.js Architecture -- Led team of 4 frontend developers, designed reusable component library, integrated advanced AI libraries (face-api.js, Mediapipe) and Monaco Editor, established scalable development workflows and code quality standards.
Results
- ✓ Reduced code duplication by 40%
- ✓ Improved development speed by 35%
- ✓ Delivered complex AI features on schedule with zero critical bugs
- ✓ Established scalable architecture supporting future expansion





