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kumar-kiran-24/README.md

Hi, I'm Kiran Kumar S

Software Developer | AI/ML & Generative AI

I'm an Information Science & Engineering student interested in building reliable software systems and AI-powered applications.

I work across backend development, machine learning, Generative AI, and cloud technologies, with a focus on turning ideas into practical, end-to-end solutions.

Currently, I'm gaining hands-on software engineering experience while working with technologies such as Python, FastAPI, PostgreSQL, AWS, Docker, LLMs, and RAG.

What I Build

  • Backend services and REST APIs
  • AI/ML applications and predictive systems
  • LLM-powered applications
  • Retrieval-Augmented Generation (RAG) systems
  • Data processing and analytics pipelines
  • AI-powered automation and reporting systems
  • Scalable applications using cloud and container technologies

Tech Stack

Languages

Python Java JavaScript

Backend & Databases

FastAPI Flask PostgreSQL REST API

AI / Machine Learning

PyTorch TensorFlow Scikit Learn Pandas NumPy

Generative AI

LangChain Hugging Face RAG LLMs

Cloud & Development Tools

AWS Docker Git GitHub Linux

Featured Projects

TASKVERSE — AI-Powered Assessment & Placement Platform

Contributed to an AI-powered assessment and placement platform involving online assessments, coding evaluations, analytics, proctoring, and AI-driven reporting.

Key areas:

  • Backend services and REST APIs
  • FastAPI and PostgreSQL
  • AI-powered analytics and reporting
  • LLM-based student performance analysis
  • Data visualization and automated PDF reports
  • Microservices and cloud deployment

Technologies: Python · FastAPI · PostgreSQL · AWS · Docker · LLMs · Generative AI · REST APIs

RAG-Based AI Chatbot

Built a document question-answering system using Retrieval-Augmented Generation to provide context-aware responses from uploaded knowledge sources.

Pipeline:

Documents → Chunking → Embeddings → Vector Search → Retrieval → LLM → Response

Technologies: Python · LangChain · Hugging Face · Sentence Transformers · FAISS · RAG · LLMs · Flask

Video-to-Notes & AI Chatbot

Developed an end-to-end AI application that converts video or YouTube content into structured notes and enables users to interact with the generated knowledge through a chatbot.

Pipeline:

Video/YouTube → Audio → Text → Notes → PDF → RAG → Chatbot

Technologies: Python · MoviePy · yt-dlp · LangChain · FAISS · LLMs · Flask · Next.js

Currently Learning

  • Advanced Generative AI
  • Large Language Models
  • RAG and AI Agents
  • Deep Learning with PyTorch
  • Backend Architecture
  • Cloud & Distributed Systems

GitHub Stats

Connect With Me

Building software. Exploring AI. Solving real problems.

Pinned Loading

  1. Automated-Attendance-System Automated-Attendance-System Public

    A complete AI-powered Smart Attendance System

    Python 6

  2. chatbot chatbot Public

    Built a FastAPI backend for an AI chatbot with RAG capabilities, supporting website, PDF, and text data ingestion. Implemented FAISS-based retrieval with embeddings and integrated a Groq LLM for co…

    Python 5

  3. Codebase-AI-Assistant Codebase-AI-Assistant Public

    Built an AI Codebase Assistant leveraging RAG, vector databases (Qdrant), and LLMs to enable intelligent querying and understanding of software repositories.

    Python 2

  4. ai-video-summarizer ai-video-summarizer Public

    Convert YouTube videos or local video files into clean notes (PDF/TXT) and interact with an AI chatbot that understands the video content.

    Python 1

  5. machine-learning-project machine-learning-project Public

    An end-to-end machine learning system that predicts a student's marks using features such as study hours, past performance, attendance, and behavioral/academic patterns.

    Jupyter Notebook 6