Branding
ResuMatch — LLM-Powered AI Ranking Platform
A generative AI candidate ranking platform using GPT-4, PyTorch, and transformer-based NLP with FastAPI-based MCP/agent workflows — reducing end-to-end screening time by 60%.
Year :
2025
Industry :
AI / Machine Learning
Client :
Personal Project
Project Duration :
3 months
Problem :
Recruiters spend enormous time manually screening hundreds of resumes for each role. Traditional keyword-matching systems miss semantic relevance and fail to surface the best candidates — leading to slow hiring cycles and missed talent.
Solution :
ResuMatch uses GPT-4 and PyTorch transformer models to semantically embed both resumes and job descriptions, then ranks candidates by cosine similarity. A FastAPI-based MCP/agent workflow automates the ingestion, embedding, and scoring pipeline end-to-end.
Cloud-native Docker containers deployed on AWS S3, EC2, and SageMaker handle large-scale ingestion and automated model deployment, reducing candidate screening time by 60%.
Tech Stack :
GPT-4 · PyTorch · Transformer-based NLP · Semantic Embeddings · FastAPI · MCP/Agent Workflows · AWS S3, EC2, SageMaker · Docker · AI Security Validation (Guardrail)
Impact :
ResuMatch reduced end-to-end candidate screening time by 60% and demonstrated how generative AI and scalable MLOps pipelines can meaningfully transform high-volume recruiting workflows. The project showcases production-ready LLM deployment with Guardrail-style AI safety validation.
