Agentic AI & RAG Systems

Explore my projects in agentic AI systems, Retrieval-Augmented Generation (RAG), and multi-agent orchestration for production-ready AI applications.

Agentic Clinical Assistant

A production-ready, regulated-domain-safe agentic RAG system that orchestrates multiple specialized agents for clinical operations:

  • Multi-Agent Orchestration: Coordinates multiple specialized AI agents to handle complex clinical workflows
  • Regulated-Domain Safety: Built with strict compliance and safety requirements for healthcare applications
  • Retrieval-Augmented Generation (RAG): Combines document retrieval with LLM generation for accurate, context-aware responses
  • Strict Grounding: Ensures all responses are grounded in verified clinical documentation
  • Auditability: Complete traceability of agent decisions and information sources
  • Production-Ready: Deployed with enterprise-grade security and monitoring

Technologies: Python, RAG, LLM, Multi-Agent Systems, Healthcare AI, Safety & Compliance Frameworks

Key Features: Regulated-domain compliance, multi-agent coordination, production deployment


Azure GenAI - RAG System

A FastAPI-based RAG application integrating Azure OpenAI and Azure AI Search:

  • RAG-Powered Q&A: Context-aware question answering using document retrieval and GPT-4o
  • Vector Search: Semantic document retrieval using Azure AI Search
  • Embedding Generation: Text embeddings using Azure OpenAI text-embedding-3-small model
  • Chat API: Direct conversational interface with GPT-4o
  • Multi-Agent Workflows: Complex AI orchestration for advanced use cases
  • Production Deployment: Docker containerization and Kubernetes support

Technologies: Python, FastAPI, Azure OpenAI, Azure AI Search, GPT-4o, RAG, Vector Search, Docker

Key Features: Enterprise RAG system, scalable architecture, cloud-native deployment


Agentic AI & RAG Expertise

  • Multi-Agent Systems: Design and orchestration of specialized AI agents for complex workflows
  • RAG Architecture: Retrieval-Augmented Generation systems for context-aware AI applications
  • LLM Integration: GPT-4o, Azure OpenAI, and other large language models
  • Vector Databases: Azure AI Search, semantic search, embedding-based retrieval
  • Safety & Compliance: Regulated-domain AI systems with strict grounding and auditability
  • Production Deployment: Enterprise-grade RAG systems with monitoring and security

Key Capabilities

  • Design and implement multi-agent AI systems for complex problem-solving
  • Build production-ready RAG systems with document retrieval and LLM generation
  • Ensure safety and compliance in regulated domains (healthcare, finance)
  • Deploy scalable agentic AI systems with proper monitoring and observability
  • Integrate vector search and semantic retrieval for accurate context generation