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.
CareerOps
AI-powered job tracking that automates opportunity discovery, ranking, and outreach preparation.
Overview
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
Job Sources
LinkedIn • remote • niche
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Data Collector
scraping • normalization • dedup
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AI Analyzer
fit scoring • company research • role matching
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Knowledge Base (SQLite + Vector)
context • history • opportunities
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Output Surfaces
dashboard • export • notifications
CareerOps pipeline from job sources to prioritized opportunities.
Technology Stack
Technologies
Python
SQLite
Ollama
scraping
AI analysis
local LLMs
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.
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