Duration: May 2026 – July 2026
Role: AI Research Intern
Research Domain: Information Retrieval, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Hybrid Search, Explainable AI

Overview

Worked as an AI/ML Intern at CoreGen, contributing to the research and development of Generative AI and Computer Vision applications. Developed Retrieval-Augmented Generation (RAG) systems, semantic search pipelines, and deep learning solutions while building end-to-end AI applications using modern LLM frameworks, vector databases, and full-stack technologies.

Tech Stack

Projects

LLM-powered Knowledge Assistant

Developed a domain-specific Retrieval-Augmented Generation (RAG) system capable of answering natural language queries using external knowledge sources.

Key Contributions

• Developed Retrieval-Augmented Generation (RAG) pipelines for knowledge-grounded question answering.
• Implemented semantic search using vector embeddings with FAISS and ChromaDB.
• Designed document ingestion, chunking, embedding generation, and retrieval workflows.
• Integrated Large Language Models with retrieval pipelines for accurate response generation.

Satellite Image Super-Resolution

Designed deep learning models for enhancing low-resolution satellite imagery to improve spatial resolution and visual quality.

Key Contributions

• Developed super-resolution models using deep learning techniques.
• Applied Computer Vision algorithms for image reconstruction.
• Improved image quality for downstream geospatial analysis tasks.

Full-Stack AI Dashboard

Built a production-ready dashboard for visualizing AI predictions and monitoring inference results.

Key Contributions

• Developed a MERN-based dashboard for AI model visualization.
• Integrated backend inference APIs with interactive frontend analytics.
• Designed responsive interfaces for displaying prediction outputs and model insights.

Key Contributions

• Developed scalable Retrieval-Augmented Generation (RAG) pipelines for enterprise AI applications.
• Built semantic search systems using vector embeddings and FAISS / ChromaDB.
• Designed modular LLM workflows using LangChain and LlamaIndex.
• Implemented REST APIs for AI inference and backend integration.
• Worked on Computer Vision pipelines for satellite image enhancement.
• Developed interactive dashboards for AI model monitoring and visualization.
• Optimized data preprocessing and model inference workflows for improved efficiency.
• Collaborated using Git-based software development practices within an agile development team.

Skills Demonstrated

• Retrieval-Augmented Generation (RAG)
• Large Language Models (LLMs)
• Semantic Search
• Vector Databases
• Prompt Engineering
• LangChain
• LlamaIndex
• Hugging Face Transformers
• Computer Vision
• Deep Learning
• Satellite Image Processing
• REST API Development
• MERN Stack Development
• Docker
• Linux Development
• Git & GitHub
• AI System Deployment