Ai Engineer
CurrentAI-Powered Mock Interview System for UPSC AspirantsObjective : Build a system that simulates a job interview environment, where students can practice and get feedback on their answers performance.Key FeaturesQuestion Generation: Dynamically generates interview questions based on academic background, location, current affairs.Feedback System: Provides detailed feedback on responses, and suggests areas of improvement.Tech Stack: Python, GPT-4, ChromaDB, React (react-speech-recognition) for speech to text convert, also react use for text to speech. AI Counsellor for Educational Website• Tech Stack: Python, GPT-4, ChromaDB, NLP, Automation Tools• Objective: Streamline AI-driven student support while reducing the support team from 5-7 people to 1-Developed Large Language Model (LLM) from Scratch• Objective: 124 million parameter LLM for versatile text generation.• Technologies Used: Python, Transformer Architecture, PyTorch.Fine-tuning LLM for UPSC-MCQ Generation• Technologies Used: LLM (base model), PEFT, Tokenizer, Hyperparameter, Dataset, prompts• Objective: Reducing Teacher Dependency in Generating UPSC-Level MCQ• Achievements: Achieved 60% cost reduction and generated over 2000 high-quality MCQs. Enhanced relevance and accuracy, level of difficult and quality of generated questions are 80 -90% . Estimated time savings of 150+ hours for question creators. Question Answering System using RAG and ChromaDB• Objective: Develop a QA system to extract accurate answers from PDF databases.• Technologies Used: ChromaDB, LangChain, RAG, PDF parsing libraries.• Achievements: saved 50% of time on manual searches.Student Performance Prediction• Objective: Develop a system to predict student academic performance based on past grades, attendance, and behavioral data.• Tech Stack: Regression algorithms (e.g., Linear Regression, Random Forests), Hyperparameter optimizer.• Impact: Helps educators identify students who may need additional support.