Jobably - AI Job Discovery
An AI-assisted job-search product that gathers opportunities, matches listings to user preferences, filters duplicates, and helps organize applications. Tijani participated in the product's development and deployment.
Jobably streamlines job discovery by collecting listings from multiple sources, comparing them with user preferences, removing duplicate opportunities, and ranking relevant roles. Its product scope also includes tracking applications as candidates move through a search process. Tijani participated in developing and deploying the product, with engineering contributions in AI Job Discovery, Data Collection, Preference Matching, Duplicate Detection, Ranking.
The problem
Job seekers often search across many sources and encounter repeated or poorly matched listings. A consolidated workflow helps reduce repetitive review and makes it easier to focus on relevant opportunities.
Tijani's development and deployment role
Tijani participated in developing and deploying the AI job-discovery product, contributing to the application workflows around listing discovery, preference-based matching, duplicate handling, ranking, and application organization.
- Collect job listings from supported sources.
- Compare opportunities against candidate preferences.
- Detect and remove duplicate postings.
- Rank relevant roles and organize application progress.
Product workflow and implementation
Jobably streamlines job discovery by collecting listings from multiple sources, comparing them with user preferences, removing duplicate opportunities, and ranking relevant roles. Its product scope also includes tracking applications as candidates move through a search process. The implementation and deployment scope described for this engagement covers the product workflows and engineering areas listed below.
- AI Job Discovery
- Data Collection
- Preference Matching
- Duplicate Detection
- Ranking
- Application Tracking
Engineering scope
Tijani's participation connected the product requirements to working application functionality and deployment. The supplied project inventory identifies AI Job Discovery, Data Collection, Preference Matching, Duplicate Detection, Ranking, Application Tracking as the relevant implementation areas. Where a specific framework, database, or performance metric was not supplied, this case study does not invent one.
Technologies & implementation areas
- AI Job Discovery
- Data Collection
- Preference Matching
- Duplicate Detection
- Ranking
- Application Tracking
Role & ownership
Tijani participated in developing and deploying the AI job-discovery product, contributing to the application workflows around listing discovery, preference-based matching, duplicate handling, ranking, and application organization. This description reflects Tijani's participation in the product's development and deployment; it does not imply that he was the sole developer, designer, or owner of the wider product.