CareerLens.ai

CareerLens AI understands your strengths, finds your gaps, and builds a clear learning path to help you skill up and reach your dream role with purpose.

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Inspiration

After graduating from university in May, I started applying for jobs on platforms like LinkedIn, Indeed, and Simplify. I faced constant rejections, and most roles required skills that weren’t taught in college. To bridge that gap, I turned to YouTube, Coursera, and DataCamp but I quickly realized how confusing and time-consuming it was to figure out which skills or videos were actually relevant.

That frustration sparked CareerLens AI a tool that helps job seekers like me instantly understand why their resumes aren’t working, what roles they’re truly fit for, and what they need to learn next. It turns confusion into clarity with data-driven insights.

What it does?

CareerLens AI analyzes your resume, identifies your strengths and weaknesses for target roles, and recommends jobs that actually match your current profile. It then builds a personalized 7-day learning roadmap with curated courses from DataCamp, Udemy, and Coursera guiding you step-by-step to close your skill gaps.

It also generates evidence-based resume bullets, elevator pitches, and cover letters using OpenAI’s GPT-4 all grounded in your real experience.

How we built it?

We built the platform using a modern, full-stack architecture focused on speed, scalability, and privacy.

Frontend: React 18 + TypeScript + Vite Tailwind CSS for styling Zustand for global state Framer Motion for animations Recharts for progress visualization Firebase Authentication Amplitude Analytics for behavior tracking

Backend: FastAPI (Python 3.11) Anthropic Claude for resume analysis OpenAI GPT-4 for resume tailoring Firestore (Google Cloud) for database RapidAPI LinkedIn Jobs for live job search ReportLab for generating downloadable PDFs

Architecture workflow: Resume → PII Redaction → AI Analysis (Claude) → Job Matching → Tailored Resume (GPT-4) → Learning Plan → Analytics (Amplitude)

Challenges we ran intos?

Integrating many services: Combining Anthropic, OpenAI, Firebase, and RapidAPI into one smooth user flow required patience, async requests, and careful debugging. Integrating multiple AI models: Managing seamless handoffs between Anthropic Claude, OpenAI GPT-4, and Dedalus APIs required structured JSON parsing and retry logic.

Privacy protection: Ensuring no personal information (emails, phone numbers, etc.) was sent to external APIs implemented full PII redaction and SHA256 hashing.

API inconsistencies: RapidAPI job endpoints often returned incomplete data, which we handled with fallbacks and skill-based similarity scoring.

Time constraints: Balancing hackathon speed with production-grade reliability was tough every endpoint needed robust validation and async optimization.## Accomplishments that we're proud of

What we learned

How to orchestrate multiple AI models responsibly and efficiently. The importance of privacy-first design protecting user data while using external APIs. How to measure engagement with Amplitude Analytics and use real metrics for iteration. That a small, well-focused idea can genuinely help thousands of job seekers struggling with rejections.

What's next for CareerLens.ai

Technical Interview Preparation: Integrate voice-based technical mock interviews with real-time AI feedback.

Career Progress Tracking: Let users visualize how their skill score evolves over time.

Mobile App: Extend accessibility to mobile with push notifications for job matches.

Resume Benchmarking: Compare resumes against top-performing candidates in similar roles.

We also plan to add multi-language support so people from around the world can use it, regardless of background.

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