Proxima AI is an AI-powered resume analyzer and career intelligence platform built to help students and job seekers understand their career readiness. It analyzes resumes, identifies role-based skill gaps, generates placement readiness scores, creates personalized learning paths, saves career reports, and allows users to track learning progress over time.
- Resume upload and analysis
- Role-based skill gap detection
- Placement readiness score
- Personalized learning path generation
- Career intelligence report
- Saved reports using MongoDB and AWS S3
- Dashboard to view previous saved reports
- Learning progress tracker
- Secure authentication using Clerk
- Delete functionality for analyses and saved reports
Frontend: React, Vite, Tailwind CSS, Framer Motion
Backend: Node.js, Express.js
Database: MongoDB, Mongoose
Authentication: Clerk
Cloud Storage: AWS S3
Tools: Git, GitHub, VS Code
Proxima AI uses AWS S3 to store generated career intelligence reports securely in cloud object storage. MongoDB stores report metadata, while the complete report data is saved in a private S3 bucket.
This demonstrates practical cloud concepts such as:
- Object storage
- IAM-based access
- Backend-to-cloud integration
- Secure report storage
- Application data separation
Proxima AI is being improved as a cloud-hosted full-stack application to demonstrate practical AWS and DevOps skills.
- Frontend deployment using Amazon S3 + CloudFront
- Backend deployment on Amazon EC2
- Reverse proxy setup using Nginx
- Process management using PM2
- Saved career reports stored in AWS S3
- Metadata stored in MongoDB
- Monitoring and logs using Amazon CloudWatch
- Infrastructure provisioning using Terraform
- CI/CD automation using GitHub Actions
The project includes an infra/ directory for Terraform-based AWS infrastructure provisioning.
Planned Terraform resources include:
- EC2 instance for backend hosting
- Security Group for controlled access
- SSH Key Pair for server access
- IAM permissions for AWS service integration
- Output values such as public IP and instance details
This improves the project from a normal full-stack application into a production-style cloud application with deployment, automation, monitoring, and infrastructure management.
This project was developed as a final-year B.Tech CSE group project under the GitHub organization proxima-ai-team.
- Chayan Bez
- MD Jahid Alam
- Tushar Pal
- Sovandev Panchadhyaee
- Chandra Dalal
- Saurabh Banerjee
NSHM Knowledge Campus Durgapur
B.Tech CSE, MAKAUT
As a contributor to Proxima AI, my major work included:
- Integrated AWS S3 for storing generated career intelligence reports
- Implemented saved career reports using MongoDB metadata and AWS S3 object storage
- Built the dashboard section to view previous career reports
- Developed the saved career report page
- Added the learning progress tracker with skill completion checklist
- Implemented delete functionality for resume analyses and saved reports
- Improved the “Why Proxima AI” comparison section
- Worked on role-based career intelligence flow and UI improvements