The Brief
Job search apps tend to optimize for listing volume, not relevance — the result is a firehose of postings a candidate has to manually filter. AI Smart Job Finder was built around the opposite bet: fewer, better-matched results, using AI to do the matching work a job seeker would otherwise do manually across a dozen browser tabs.
The Approach
As with Kalori AI, the app was built solo using an AI-accelerated workflow — from initial scaffolding through to the matching and recommendation logic — which is what made a focused mobile product achievable on a solo developer's timeline instead of requiring a team.
Where AI Fit In
AI is used in the matching layer: interpreting a candidate's profile and preferences against available listings to surface relevance, rather than presenting a raw, unranked feed. As with any AI-assisted recommendation system, the design goal was to keep the logic legible — showing why a match was suggested — rather than a black-box score.
The Outcome
AI Smart Job Finder shipped to Google Play under the ZaidanLab developer account. Alongside Kalori AI, it's a second proof point for the same underlying approach: AI accelerates the build, a senior developer owns the product and architecture decisions.