LLM Job Tracker

AI-powered job application tracker with automated note generation.

Track job applications, monitor pipeline status, and generate tailored insights like "Why I Fit", recruiter messages, and interview checklists using LLMs.

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Features

• Track job applications with status (applied, interviewing, offer)
• Dashboard with pipeline stats
• Search and filter applications
• Detailed application view
• AI-generated:
  • Why I Fit
  • Recruiter message
  • Interview checklist
• Background processing with Celery + Redis
• Local LLM support via Ollama

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Tech Stack

• Backend: FastAPI, Python
• Database: MongoDB
• Async Tasks: Celery, Redis
• Frontend: Next.js, TypeScript, TailwindCSS
• AI: Ollama, LLM

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Screenshots

Focused UI crops (not full-page captures) so text and controls stay readable on GitHub.

Dashboard Overview

Overview of application tracking, pipeline stats, and creation flow.

Notes Generation (Before)

Selected application before AI-generated notes are created.

Notes Generation (After)

Selected application after AI-generated notes are created.

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Architecture

Frontend (Next.js) -> FastAPI -> MongoDB
FastAPI -> Celery + Redis -> Ollama (LLM)

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Running Locally

1. Clone the repository

git clone https://github.com/onurozko/LLM-Job-Tracker-API.git
cd LLM-Job-Tracker-API

2. Start services

docker compose up --build

3. Access

• Frontend: http://localhost:3000
• API docs: http://localhost:8000/docs

From frontend/, run npm install then npm run dev (use frontend/.env.example as frontend/.env.local). Copy root .env.example to .env before Docker; set CORS_ORIGINS=http://localhost:3000 for the dashboard.