CareerIQ

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Graph Visualization
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Architecture

Company: ADP

Hackathon Challenge: AI-Powered Career & Compensation Navigator

Inspiration

Navigating career growth can be overwhelming, especially with ever-changing job markets, evolving skill demands, and varying compensation trends. We wanted to create a data-driven AI assistant that empowers users to make informed career decisions, explore opportunities, and visualize potential career paths with ease.

What it does

CareerIQ is an interactive AI-powered career assistant that: Analyzes compensation trends based on industry and role. Provides data-driven career advice by simulating various career paths. Visualizes opportunities using dynamic charts and insights. Answers career-impacting questions with AI-powered analysis. Ensures privacy and security while handling sensitive career-related data.

How we built it

Frontend: Developed using Streamlit for an intuitive and responsive UI. Backend: Using python, openAI, gpt-4o-mini, LangChain, Presedio Data Visualization: Implemented Plotly for interactive career simulations. APIs & Data Sources: Integrated job market data, salary benchmarks, and skill demand trends.

Challenges we ran into

Handling large-scale career data while ensuring real-time responses. Optimizing AI-generated insights to provide reliable and actionable advice. Designing an intuitive user experience to make career exploration seamless. Ensuring data privacy while handling sensitive career-related information.

Accomplishments that we're proud of

Successfully built a functional AI career assistant in a limited time. Created engaging data visualizations that help users explore career options dynamically. Integrated real-world job market trends to offer relevant insights. Developed an interactive simulation tool for career progression analysis

What we learned

Integrating LLM, AI and RAG Identifying PII for Security using NLP.

What's next for CareerIQ

Expanding data sources to include more job market and salary databases. Enhancing AI-driven recommendations with machine learning models. Developing a mobile-friendly version for seamless career exploration. Adding personalized career coaching features for tailored guidance.