Intelligent Interview System
Low-latency candidate evaluation platform utilizing multi-agent AI consensus pipelines.
Problem
Existing technical hiring platforms face functional fragmentation, scalability constraints under high concurrent user loads, and static assessments that fail to adapt in real-time to candidate performance.
Approach
Co-architected a full-stack low-latency platform using the MERN stack and Google Gemini API. Engineered a multi-agent MockLLM framework to dynamically adjust questioning, backed by Redis caching and BullMQ job queues for async orchestration. Integrated WebRTC and Socket.io with Monaco Editor for a real-time collaborative workspace.
Outcome
Delivered bidirectional code synchronization with <70ms latency and a multi-language code execution engine boasting a 1.4s average response time. Reduced overall API latency by 46% (890ms to 420ms) and improved candidate answer quality by 12.3% during pilot evaluations.
Technical Highlights
(7)- 01
Multi-agent MockLLM framework dynamically adjusts questioning difficulty based on real-time candidate performance analysis
- 02
WebRTC + Socket.io integration delivers bidirectional code synchronization with <70ms latency
- 03
Monaco Editor provides full IDE experience with syntax highlighting and multi-language support
- 04
Redis caching + BullMQ job queues handle async orchestration and high concurrent loads
- 05
Multi-language code execution engine with 1.4s average response time
- 06
API latency reduced by 46% (890ms → 420ms)
- 07
Candidate answer quality improved by 12.3% in pilot evaluations