LinkedIn / 2024
Recruiter AI Agent
Designed the AI agent experience for LinkedIn Recruiter, translating conversational assistance into a complex enterprise recruiting workflow.
Role
Principal Designer
Why it mattered in the deck
In the interview deck, this project signaled present-day relevance. It showed that my portfolio was not only about playful consumer work, but also about AI and enterprise complexity.
Context
At the time of the presentation, AI was the company's highest priority. Recruiter AI Agent anchored my work directly inside that shift.
Challenge
Design an agent experience that feels genuinely helpful inside a professional recruiting workflow rather than feeling like AI theater layered onto an enterprise product.
Role and scope
Principal designer on the Recruiter AI agent experience.
Worked on enterprise-facing AI interaction patterns and task framing.
Balanced workflow efficiency with clarity, confidence, and control.
Supporting materials from the original deck
Deck overview mention
The original presentation used Recruiter AI Agent as the AI proof point inside the four-project overview.
Process
Map AI to a real enterprise workflow
The design challenge was less about novelty and more about making AI assistance useful, legible, and trustworthy inside an existing recruiter workflow.
Design for confidence and control
Enterprise users need clear expectations, not magic. The interaction model had to help recruiters understand what the system was doing and why it was useful.
Design Decisions
Avoid AI as spectacle
The work prioritized task clarity and workflow value over flashy chat surfaces or generic assistant tropes.
Keep the human in control
The design emphasized guidance and acceleration rather than opaque automation, which is especially important in high-stakes recruiting contexts.
Outcomes
AI relevance
The project grounded the portfolio in the market's most current product shift and complemented the broader LinkedIn story.
Enterprise range
It balanced the consumer and vision work in the deck by showing fluency with enterprise product complexity.
This project functioned as a present-tense signal inside the original portfolio and remains intentionally concise here until deeper source material is added.