NextStep

AI-Powered Career Recommendation and Learning Platform

NextStep is a full-stack AI-powered platform designed to help students, interns, and aspiring professionals identify technology career paths that align with their skills, interests, education, and experience.

The platform combines a TensorFlow-based recommendation model with AI-powered guidance to transform a user profile into actionable career insights. Instead of simply suggesting a job title, CareerPath AI provides career match scores, identifies skill gaps, and generates personalized learning roadmaps to help users move toward their career goals.

This project demonstrates the integration of Machine Learning, Generative AI, backend APIs, modern frontend development, authentication, and cloud-based data storage in one complete application.

Key Highlights

• Machine Learning-based career recommendation system
• Personalized career match scores
• Skill-gap analysis based on career requirements
• AI-generated learning roadmaps
• Interactive user dashboard and progress tracking
• Secure authentication and persistent user data
• Full-stack architecture with separate frontend and backend services

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Screenshots

Landing Page

Profile and Skill Assessment

Career Recommendations

recommended roles

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How It Works

1. The user creates a profile by providing skills, interests, education, and experience information.

2. The backend processes the profile and prepares the input features for the recommendation model.

3. The TensorFlow model analyzes the profile and predicts suitable career paths.

4. The platform returns personalized career recommendations with match scores.

5. Career requirements are compared with the user's current skills to identify skill gaps.

6. AI generates a structured learning roadmap to help the user develop the required skills.

7. User progress and learning history are stored securely using Firebase.

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System Architecture

                         Next.js Frontend
                   React and Tailwind CSS
                                |
                                |
                                v
                         FastAPI Backend
                    APIs and Business Logic
                                |
               -------------------------------
               |              |              |
               v              v              v
        TensorFlow Model    Groq AI       Firebase
        Career Matching     Roadmaps      Auth and DB

The frontend provides the user interface, while FastAPI manages application logic and communication with the AI and Machine Learning services. Firebase handles authentication and persistent user data.

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Technology Stack

Category	Technologies
Frontend	Next.js, React, TypeScript, Tailwind CSS
Backend	Python, FastAPI
Machine Learning	TensorFlow, NumPy, Pandas
AI Integration	Groq API
Database	Firebase Firestore
Authentication	Firebase Authentication
Deployment	Vercel, Render

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Machine Learning Model

The career recommendation engine is built using TensorFlow and analyzes user profile features to predict suitable technology career paths.

The model considers information such as:

• Technical skills and proficiency levels
• Career interests
• Educational background
• Experience level
• Skill relationships between different career domains

The model supports recommendations across multiple technology roles, including:

• AI Engineer
• Machine Learning Engineer
• Data Scientist
• Data Analyst
• Backend Developer
• Full Stack Developer
• Frontend Developer
• Cloud Engineer
• DevOps Engineer
• Cybersecurity Analyst
• Computer Vision Engineer
• NLP Engineer
• Data Engineer
• Software Engineer
• AI Product Engineer

Project Structure

careerpath-ai/

├── frontend/
│   ├── app/
│   ├── components/
│   ├── services/
│   └── types/
│
├── backend/
│   ├── routes/
│   ├── services/
│   ├── models/
│   ├── ml_integration/
│   └── data/
│
├── ml/
│   ├── dataset/
│   ├── train.py
│   ├── evaluate.py
│   └── saved_model/
│
├── screenshots/
│
├── README.md
└── LICENSE

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Installation

Clone the Repository

git clone https://github.com/Aiman-Wazir/nextstep

Backend Setup

cd backend

python -m venv venv

Activate the virtual environment.

Windows:

venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Run the backend:

python main.py

The backend will run at:

http://localhost:8000

Frontend Setup

cd frontend
npm install
npm run dev

The frontend will run at:

http://localhost:3000

API documentation is available at:

http://localhost:8000/docs

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Environment Variables

Create a .env file inside the backend directory:

GROQ_API_KEY=your_api_key
ENVIRONMENT=development
PORT=8000
ALLOWED_ORIGINS=http://localhost:3000

Create a .env.local file inside the frontend directory:

NEXT_PUBLIC_API_URL=http://localhost:8000/api/v1

NEXT_PUBLIC_FIREBASE_API_KEY=your_api_key
NEXT_PUBLIC_FIREBASE_AUTH_DOMAIN=your_project.firebaseapp.com
NEXT_PUBLIC_FIREBASE_PROJECT_ID=your_project_id

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API Endpoints

Method	Endpoint	Description
GET	/api/v1/health	Check API status
GET	/api/v1/careers	Get available careers
POST	/api/v1/recommend	Generate career recommendations
POST	/api/v1/skill-gap	Analyze skill gaps
POST	/api/v1/roadmap	Generate learning roadmap
GET	/api/v1/progress/{user_id}	Get learning progress
PUT	/api/v1/profile/{user_id}	Update user profile

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Future Improvements

• Integration with real-world job market data
• LinkedIn profile analysis
• Resume analysis
• Additional career categories
• AI-powered interview preparation
• Personalized course recommendations
• Mobile application

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Limitations

The current recommendation model is trained using synthetic data and is designed primarily as a demonstration of an AI-powered career recommendation workflow. Future versions can be improved by incorporating real-world user feedback, job market datasets, and larger career datasets.

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Author

Aiman Wazir

Full Stack and AI Developer

License

This project is licensed under the MIT License.