I am a Computer Science undergraduate at Jain (Deemed-to-be University), Bengaluru, building at the intersection of AI and backend engineering.
My focus is on multi-agent orchestration — designing systems where multiple LLMs, data pipelines, and services work in coordination to solve real problems. I do not experiment in notebooks. I ship live, deployed applications.
Location : Bengaluru, Karnataka, India
Degree : B.Tech Computer Science (2024 – 2028)
Focus : LLM Orchestration · RAG Pipelines · Backend APIs
Currently : Building AI systems that work in production
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Limbi — Omni-Agent Orchestration Platform A production-grade multi-agent orchestration platform with 89 specialized agents and 435 available actions. One command routes across engineering, security, cloud, DevOps, finance, and domain agents. Supports 19 LLM provider modes — local (Ollama, LM Studio, vLLM) and cloud (OpenAI, Anthropic, Groq, OpenRouter). Features graph-backed session memory, RAG pipelines, FastAPI backend, MCP server, and VS Code extension. |
Multi-AI-Models Chat Support System A secure platform orchestrating parallel responses from multiple LLMs simultaneously. Unified API abstraction across Groq, OpenRouter, Bytez, and Chutes with Tavily-powered real-time web research. |
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Hike.ai — Unified AI Orchestration Platform Five production AI modules under one roof: News Flow with semantic search, multi-model Debate Arena, Regret AI for decision analysis, and Empathy AI — secured with Google OAuth 2.0 and bcrypt auth. |
Local Knowledge Chatbot — RAG System Fully offline RAG chatbot running entirely on-device. Zero external API dependencies. Automated scraping pipelines feed ChromaDB; Ollama serves local LLMs (Mistral, LLaMA). One-command Docker deployment. |
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Sales Forecasting ML Pipeline End-to-end ML pipeline reading directly from structured databases — no manual exports. Automated feature engineering and model evaluation loops benchmark regression and tree-based models on sales time-series data. |
NLP Transformation Engine Advanced NLP pipeline that converts AI-generated text into natural, human-like writing. Configurable humanization, summarization, and readability optimization with real-time AI pattern detection across 18 signal types. Style-adaptive output targeting Casual, Professional, Academic, and Concise registers. |
Languages & Frameworks
AI / ML
Agent Orchestration
Databases
Foundations
DevOps, Streaming & Observability
