JobJet
A scalable job-matching platform facilitating seamless recruiter-candidate interactions with secure resume uploads and user profile management.
Core Technologies
Key Outcomes
Project Overview
Challenge
Matching candidates efficiently from large datasets. Solved by independently researching and implementing an optimized string search and sorting algorithm in Python.
Execution
Implemented robust architecture leveraging state-of-the-art patterns to solve the core bottlenecks and ensure seamless delivery.
Key Achievements
Delivered 100% on time
Zero downtime during migration
Highly scalable architecture
Visual Gallery
Solutions Architecture
High-level overview of the system architecture, detailing service boundaries, infrastructure deployment, and core data flow.
Component-driven frontend using React.js and Tailwind CSS
Backend infrastructure for application tracking and resume uploads
Optimized string search and sorting algorithm in Python for candidate-job matching
System Internals
Deep dive into the architectural layers, data models, and interface designs that power the application.
| Field Name | Data Type | Modifiers & Constraints |
|---|---|---|
| id | UUID | PK, Default(gen_random_uuid()) |
| user_id | UUID | FK (users.id), Index |
| status | Enum | Default('pending') |
| metadata | JSONB | Nullable |
| created_at | Timestamp | Default(now()) |
Challenges & Optimizations
Addressing performance bottlenecks, architectural friction, and the steps taken to ensure production reliability.
The Friction
Initial load times suffered due to synchronous blocking operations when fetching heavy nested relationship data from the primary database, causing a degraded experience on mobile networks.
The Optimization
Implemented a Redis-backed caching layer and decoupled the data queries using background workers. Data is now aggressively prefetched and served from memory.
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