Specializing in LLM Orchestration, Multi-Agent Systems, and Cloud-Native AI Based in Hyderabad, India 🇮🇳
I build intelligent systems that don't just predict, but reason and execute. From local, air-gapped multi-agent frameworks for secure coding to multimodal RAG pipelines on GCP, I focus on turning experimental AI into production-grade infrastructure.
- GoogleCloudPlatform/python-docs-samples: Official Contributor to Google Cloud samples.
- Engineered production-ready code for Vertex AI Agent Engine (Reasoning Engine) and Gemini AI integration.
- Refined multimodal image processing pipelines and optimized dependency management for Python-based cloud services.
- Agentic AI Researcher: Focused on self-correcting LLM loops and local inference security.
Production-ready, privacy-first RAG system for secure document intelligence.
- Impact: Built a high-performance local RAG pipeline that eliminates cloud dependencies, ensuring 100% data sovereignty for sensitive enterprise documents.
- Key Feature: Engineered a Semantic Chunking engine that splits text based on topic shifts rather than character counts, paired with LanceDB hybrid search for industry-leading retrieval accuracy.
- Tech: Next.js 15 (App Router), FastAPI, LanceDB (Vector Store), Ollama (Llama 3.2), Tailwind CSS.
Autonomous Multi-Agent System for Secure, Air-Gapped SDLC.
- Impact: Designed a local-first orchestration framework using LangGraph and Ollama to automate code generation, testing, and refactoring.
- Key Feature: Implemented a Self-Correction Loop where specialized agents (Architect, Coder, Reviewer) critique and fix code autonomously without cloud data leakage.
- Tech: Python, LangGraph, Ollama (Llama3/DeepSeek), Streamlit.
Agentic Drug Research Assistant powered by Gemini 2.0.
- Impact: Developed an intelligent agent that automates medical literature synthesis by combining Real-time Web Search with LLM reasoning.
- Key Feature: High-speed research synthesis using Gemini 2.0 Flash and DuckDuckGo integration, providing structured reports for healthcare professionals.
- Tech: Gemini 2.0 API, Python, Streamlit, DuckDuckGo Search API.
Real-time conversational AI with visual and auditory perception.
- Impact: Built a low-latency voice agent on Vertex AI capable of real-time screen processing and speech-to-speech interaction.
- Tech: Vertex AI (Gemini 1.5 Pro), WebRTC, Google Cloud Functions.
Time-series prediction for financial markets with 87% accuracy.
- Impact: Engineered ensemble ML models (LSTMs + XGBoost) for market trend prediction. Integrated data versioning and model monitoring.
- Tech: TensorFlow, Scikit-learn, Pandas, Time Series Analysis.
| Category | Tools & Technologies |
|---|---|
| Agentic AI | LangGraph, CrewAI, Multi-Agent Orchestration, Self-Correction Loops |
| LLMs & RAG | Gemini 2.0, GPT-4, Llama 3, ChromaDB, Pinecone, LangChain |
| Machine Learning | TensorFlow, PyTorch, Scikit-learn, Time Series, Computer Vision |
| Cloud & MLOps | GCP (Vertex AI, BigQuery), Docker, Ollama (Local LLMs), CI/CD |
| Backend & Data | FastAPI, NestJS, Python, SQL, Apache Spark |
I'm looking for roles at the intersection of Generative AI, Finance, and Cloud Architecture. If you're building autonomous systems, let's collaborate.
💡 Learning Focus: Scalable Agentic Workflows and DeepSeek Model fine-tuning.