AI-Powered Recruiting and Conversational Portfolio Experience
GitRoll | Design the systems behind an agentic AI recruiter and the conversational digital portfolio.
Role
UX Designer
Type
Intern + UX Design + UI Design + Research
Duration
2025 June – 2025 September
TL;DR
Problem
Both recruiters and candidates spend excessive time searching, managing connections, and interpreting fragmented information.
Solution
GitRoll introduces AI Profiles and a conversational portfolio, agent AI, powered by a shared recruiting process system.
My role
Led UX and system design across AI profile, website, and recruiter workflows.
Impact
A unified system that turns profiles and portfolios into interactive, queryable interfaces instead of static documents.
Problem space
Past
Recruiting and professional networking are still built around static artifacts(resumes, LinkedIn profiles, and traditional portfolios) that require heavy manual searching and interpretation.
Now
At the same time, AI is changing how people expect to access information: • Less browsing, more asking • Less managing connections, more meaningful signals • Less static content, more dynamic, contextual responses
Future
GitRoll explores what a future AI-native recruiting experience could look like for both recruiters and candidates.
Users
Recruiters / Hiring managers
Candidates / Engineers
THEIR JTBD
Build a high-performing team with minimal friction
Filter a massive pool of applicants down to 3-5 hirable finalists to minimize the hiring manager's time spent interviewing.
Secure a position that meets specific salary, benefit, and tech-stack requirements while minimizing the cost of the search (time spent interviewing).
CORE INSIGHT
In an AI-driven workflow, people want to spend less time searching and managing connections, and more time interpreting meaning.
This shifts product design from:
- • Pages → Systems
- • Documents → Interfaces
- • Browsing → Asking
USER FLOW SKETCH
In an AI-driven workflow, people want to spend less time searching and managing connections, and more time interpreting meaning.
Information architecture
Inputs
LinkedIn (required)
GitHub / Resume (optional)
Job Description
AI Intelligence Layer
Structured candidate profile
Project & evidence memory
Retrieval + rubric-based reasoning
Outputs
Conversational AI Profile
Recruiter workflows
Product delivery/Prototype
AI PROFILE
Goal
Enable rapid understanding of a candidate without manual scanning.
Interactive AI résumé lets employers chat with candidate avatar, revealing motivations, project context, experience stories, working styles, collaboration preferences, aspirations.
MULTI-SOURCE TALENT POOL
AGENTIC AI RECRUITER
Autonomous recruiter agent converses, defines requirements, searches talent, ranks matches, schedules interviews, continually learning from hiring outcomes and feedback.
UX FLOW
RECRUITER FLOW
Discover → Ask → Interpret → Decide
CANDIDATE FLOW
Maintain once → Share everywhere → Let system explain
Reflection
efficiency > novelty
While working on GitRoll, I had to constantly ask myself: is this feature making the AI smarter, or is it actually saving people time? I learned that in recruiting and professional evaluation, intelligence alone isn't the bottleneck. The real friction is time spent searching, managing connections, and mentally stitching information together. This insight pushed me to prioritize system-level efficiency over feature richness, fewer steps, fewer tools, and faster access to meaningful signals.
Designing AI With Restraint
One of the hardest parts of this project was deciding what the AI should not do. It was tempting to automate decisions, generate scores, or make strong recommendations. But I learned that in high-stakes contexts like hiring, over-automation can reduce trust rather than increase it. Designing GitRoll meant intentionally keeping humans in the loop and using AI to support interpretation, not replace judgment.