Portfolio Details

Each project in this portfolio reflects my hands-on experience in solving real-world problems using data science, machine learning, and full-stack development. From predictive models to intelligent web applications, these solutions are built with a focus on scalability, functionality, and impact.

Project Information

  • Project Name: Jobify AI
  • Category: Agentic AI, LLM Systems & Career Intelligence
  • Model Type: Multi-LLM Orchestration (Gemini, OpenAI) + RAG + FAISS + LangChain
  • Github Repo: Link
  • Live Link: Link

Technologies Used

This project is built using Python with FastAPI as the core backend framework and LangChain for LLM orchestration. It integrates multiple AI providers including OpenAI and Google Gemini, with optional BYOK (Bring Your Own Key) support for flexible model usage. The system uses FAISS for vector search, RAG pipelines for contextual retrieval, and WebSockets for real-time AI interview simulations. Additional components include PDF generation using ReportLab/FPDF and job data ingestion via JobSpy APIs.

Details of Project

Struggling with fragmented career tools like resume builders, job search platforms, and interview prep apps? This system unifies them into a single intelligent AI career platform.

Jobify AI Backend is a multi-module agentic AI system designed to act as a complete career assistant. It analyzes resumes, matches jobs intelligently, generates tailored improvements, and provides real-time interview simulations powered by LLM agents.

Unlike traditional tools, this system does not rely on static rules. Instead, it uses reasoning-based AI agents, retrieval-augmented generation, and structured scoring engines to deliver explainable, personalized career decisions in real time.

Project Features

  • AI-powered Job Matcher with explainable scoring (skills, experience, education, projects)
  • Multi-LLM architecture supporting OpenAI, Gemini, and BYOK model routing
  • RAG-based intelligent chatbot using FAISS vector search for contextual answers
  • Real-time AI Interview system using WebSockets with live feedback and scoring
  • Automated resume generation, enhancement, and PDF export system
  • Skill extraction engine with gap analysis, trends, and learning recommendations
  • AI-driven career roadmap generator with phased learning plans and milestones

Technical Implementation

The system is built on a modular FastAPI backend architecture where each service operates as an independent AI module. The Job Matcher uses a hybrid scoring engine combining deterministic weighting and LLM-based reasoning for explainable results. FAISS vector databases enable semantic similarity search for resumes, job descriptions, and knowledge retrieval.

Real-time interview functionality is powered by WebSockets, enabling bidirectional communication between AI interviewer and user. LangChain orchestrates multi-step reasoning flows across agents, while PDF generation pipelines create structured resumes and reports. The system also supports BYOK architecture, allowing dynamic switching between AI providers per request for flexibility and scalability.