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myCareerNext

GenAI AWS Bedrock RAG Python FastAPI

AI-Powered E-Learning Platform

Project Objective

To develop a comprehensive AI-powered e-learning platform that revolutionizes career development through personalized learning paths, intelligent content recommendations, and interactive AI-driven features including mock interviews and conversational assistance.

  • Create personalized career roadmaps based on user goals and current skills
  • Implement AI-powered mock interview system for skill assessment
  • Build intelligent in-video search capabilities using RAG technology
  • Develop conversational AI help bot for user assistance

Project Description

myCareerNext is a cutting-edge e-learning platform that combines traditional learning management with advanced AI capabilities. The platform serves as a comprehensive career development ecosystem where users can:

Personalized Career Roadmaps

AI-generated learning paths tailored to individual career goals, current skill levels, and industry requirements.

AI Mock Interviews

Interactive interview practice sessions with real-time feedback, question analysis, and performance scoring.

Intelligent Video Search

RAG-powered search functionality that allows users to find specific content within video lectures and tutorials.

Conversational AI Assistant

24/7 AI-powered help bot providing instant answers to course-related questions and learning guidance.

Technical Stack

AI & Machine Learning

AWS Bedrock Agents LangChain RAG (Retrieval-Augmented Generation) Vector Databases

Backend Development

Python FastAPI Pydantic

Cloud & Infrastructure

AWS Lambda Amazon S3 AWS API Gateway CloudFormation

Database & Storage

Qdrant DynamoDB RDS

My Contribution

Solution Architecture & Design

Led the complete system architecture design, defining microservices structure, API specifications, and integration patterns for scalable AI-powered e-learning platform.

AI Integration & Development

Implemented AWS Bedrock integration for LLM capabilities, designed and developed RAG pipeline for intelligent video search, and created conversational AI assistant using advanced prompt engineering.

Backend Development

Built robust backend services and developed RESTful APIs for all platform functionalities.

AI Mock Interview System

Designed and implemented the complete AI-powered agt based interview system including question generation, response analysis, and evaluation System.

Deployment, MLOps and DevOps

Set up CI/CD pipelines, managed AWS infrastructure using CloudFormation, and implemented monitoring and logging solutions for production deployment.

Business Outcome

Core Services & Platform Solutions

Designed and built the core services and foundational solutions that power the platform’s key offerings, streamlining user onboarding and driving seamless adoption across the system.

Faster Content Discovery

RAG-powered video search reduced time spent finding relevant content, improving learning efficiency.

Reduction in Support Queries

AI conversational assistant handled majority of user queries, reducing support team workload.

Key Business Impacts:

  • Enhanced User Experience: AI-driven personalization created more engaging and effective learning journeys
  • Scalable Architecture: Cloud-native design enabled rapid scaling to accommodate growing user base
  • Competitive Advantage: Advanced AI features differentiated the platform in the crowded e-learning market
  • Operational Efficiency: Automated content recommendations and user assistance reduced manual intervention