Executive Summary
Dual-sided workforce intelligence platform combining Collaborative Filtering and PyTorch POCs scaled to Gemini Embeddings and Firestore Vector Search for semantic candidate ranking, JD synthesis, and candidate companion tools.
The Operational Friction
High-volume hiring pipelines suffer from mismatched candidate recommendations, manual resume evaluations, and fragmented recruiter outreach, while candidates lack tailored career insights and instant guidance.
System Architecture & Engineering Solution
Architected a dual-sided workforce intelligence platform. Built initial POCs utilizing PyTorch, TensorFlow, and Collaborative Filtering models before scaling to production with Gemini Embeddings and Firestore Vector Search. For recruiters, provided semantic resume matching, KB role auto-JD generation, candidate Kanban board, and cold message email dispatch. For candidates, engineered a role recommendation portal providing salary/career path insights, ATS resume builder, cover letter generation, and a companion Telegram bot for mobile assistance.
Measurable Impact & Production Results
- Architected POC models using PyTorch, TensorFlow & Collaborative Filtering before migrating to production Gemini Embeddings
- Recruiter Dashboard: Firestore Vector Search for semantic resume ranking, role KB auto-JD generator & interactive Candidate Kanban
- Candidate Portal: Personalized role recommendations with salary benchmarks, career path insights & tailored job discovery
- AI Career Copilot & Telegram Companion Bot: Auto-generates ATS resumes, cover letters, and provides mobile candidate assistant alerts
- Recruiter Outreach: Gemini cold message template synthesis with direct-from-dashboard candidate email dispatch