Some Important Details

🚀 AI-Powered Resume Feedback System
In today’s competitive job market, building a resume that is both impactful and tailored to specific job descriptions is critical. To address this, I developed an AI-driven web application that leverages Large Language Models (LLMs)—including models like Claude Sonnet—to deliver personalized, context-aware resume feedback.
🧠 LLM-Powered Analysis The system utilizes advanced prompt engineering and structured input parsing to evaluate resumes against job descriptions, generating actionable insights on content quality, keyword alignment, and overall effectiveness.
📄 Context-Aware Matching Implements semantic comparison techniques to assess how well a candidate’s resume aligns with target roles, helping identify gaps in skills, experience, and phrasing.
⚙️ Full-Stack Integration Designed and integrated LLM capabilities into a web application workflow, enabling seamless interaction between the frontend interface and backend AI processing pipeline.
🔄 Iterative Feedback Loop Built a dynamic feedback system where users can refine their resumes through multiple iterations, receiving progressively improved suggestions based on updated inputs.
🚀 Applied AI Engineering This project reflects hands-on experience in integrating LLMs into real-world applications—focusing on prompt design, response structuring, and building scalable, user-facing AI features.