AI Career CoPilot
Multi-Agent Job Application Assistant & Kanban Tracking Engine
1. THE PROBLEM
Applying for technical jobs requires hours of manual work tailoring cover letters, matching key experience items to job specs, and keeping track of application statuses across multiple platforms.
2. TECHNICAL APPROACH & DECISIONS
1Decoupled Celery Worker Architecture
Offloaded heavy multi-agent LLM calls (discovery, cover letter generation, ATS check) to background Celery tasks backed by Redis.
Synchronous HTTP API requests
LLM generation loops take 5–15 seconds per job post. Asynchronous queue workers prevent HTTP gateway timeouts and allow users to queue multiple applications simultaneously.
2ChromaDB Vector Matching
Stored vectorized user project snippets in ChromaDB to dynamically pull the 3 most relevant project achievements for every new job description.
Static templates with variable replacement
Dynamic vector retrieval ensures that a ML job application emphasizes PyTorch experience, while a Full-Stack application automatically pulls Next.js achievements.
3. TRADE-OFFS & HONEST REFLECTION
Running background Celery workers required introducing Redis infrastructure overhead, but completely solved user experience lag during LLM generation.
4. CONCRETE OUTCOME & METRICS
Automated candidate-job alignment, custom cover letter drafting, and real-time Kanban pipeline state updates.