Project · applications
CareerOps
AI-powered job tracking that automates opportunity discovery, ranking, and outreach preparation.
Overview
CareerOps is an intelligent job search assistant built to reduce the repetitive work of tracking opportunities, researching companies, and preparing outreach. It combines automated profile scraping, AI-based opportunity scoring, and structured follow-up workflows to create a more efficient job search pipeline.
Technology Stack
Capabilities
- Automated job scraping
- Opportunity fit scoring
- Company research synthesis
- Outreach context generation
- Application tracking
- Follow-up reminders
Implementation
CareerOps is implemented as a Python-based automation system with SQLite storage and local LLM analysis via Ollama. Job profiles are scraped, normalized, scored for fit, and stored with AI-generated summaries and outreach context.
Problem
Job hunting involves repetitive work: checking listings, researching companies, tracking applications, and preparing context for outreach. This fragmentation makes it easy to miss opportunities and reduces focus on high-quality applications.
Architecture
Graph Structure
- Job Sources: LinkedIn • remote • niche
- Data Collector: scraping • normalization • dedup
- AI Analyzer: fit scoring • company research • role matching
- Knowledge Base: SQLite • vector cache • context
- Output Surfaces: dashboard • export • notifications
Job Sources LinkedIn • remote • niche │ ▼ Data Collector scraping • normalization • dedup │ ▼ AI Analyzer fit scoring • company research • role matching │ ▼ Knowledge Base (SQLite + Vector) context • history • opportunities │ ▼ Output Surfaces dashboard • export • notifications
CareerOps pipeline from job sources to prioritized opportunities.
Outcome
CareerOps reduces job search overhead by automating research, ranking opportunities, and providing context for each application.
What's Next
- Expanded source coverage
- Interview preparation materials
- Pipeline visualization
- Mobile access