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sindhusinr/README.md

Hi πŸ‘‹, I'm Sindhu R

AI Engineer | Generative AI | Agentic AI | Conversational AI


πŸš€ About Me

I’m an AI Engineer with 6 years of experience in Conversational AI, designing and developing enterprise virtual assistants and GenAI-powered chatbot solutions.

πŸ’Ό Professional experience with Google Dialogflow CX, Genesys Cloud CX, LivePerson, LLM-powered conversational solutions, REST APIs, and enterprise integrations

πŸ€– Hands-on project experience in Agentic AI, RAG, and Multi-Agent Systems

🧠 Building AI applications using Python, LangChain, LangGraph, MCP, and LangSmith

πŸŽ“ Currently pursuing an MBA in General Management

🌱 Currently focused on expanding my expertise in Generative AI and Agentic AI


πŸ› οΈ Technical Skills

πŸ€– Generative AI & Agentic AI

LangGraph | LangChain | Multi-Agent Systems | Supervisor Architecture | Tool Calling | MCP | Guardrails | Prompt Engineering | LLMs

πŸ”Ž RAG & Evaluation

Hybrid Search | Semantic Search | Vector Embeddings | Chunking Strategies | LangSmith | RAG Evaluation

πŸ—„οΈ Databases & Vector Stores

PostgreSQL | PGVector | ChromaDB

πŸ’¬ Conversational AI

Genesys Cloud CX | Genesys Virtual Agent | Google Dialogflow CX/ES | LivePerson | Kore.ai | Amelia | Dryice Lucy

πŸ’» Programming & APIs

Python | JavaScript | Node.js | REST APIs | JSON

πŸ“Š Analytics & Development Tools

BigQuery | Looker Studio | Elasticsearch | Kibana | Postman | Git | GitHub


πŸš€ Featured AI Projects

✈️ Multi-Agent Trip Planner

A supervisor-based multi-agent travel planning application built using LangGraph.

  • πŸ€– Specialized Flight, Hotel, Weather, Budget, and Itinerary agents
  • πŸ”€ Supervisor-based conditional routing
  • πŸ›‘οΈ Guardrails for validating user requests
  • πŸ”Œ MCP integration for external information retrieval
  • 🧠 PostgreSQL-backed persistent conversation state
  • πŸ” LangSmith tracing and observability

Tech Stack: Python | LangGraph | LangChain | MCP | PostgreSQL | LangSmith


πŸ“š HR Policy RAG Assistant

A Retrieval-Augmented Generation (RAG) application for answering questions from HR policy documents using retrieved context.

  • πŸ”Ž Hybrid retrieval using semantic and keyword search
  • 🧩 Document chunking and vector embeddings
  • πŸ—„οΈ ChromaDB vector storage
  • 🌐 Web-search fallback when internal knowledge is insufficient
  • πŸ“Š RAG evaluation using LangSmith

Tech Stack: Python | LangChain | ChromaDB | LangSmith | Redis


🎯 Current Focus

πŸ€– Agentic AI & AI Agents

🧠 Multi-Agent Systems

πŸ“š Retrieval-Augmented Generation (RAG)

πŸ”Œ MCP & Tool Calling

πŸ”€ LangGraph Workflow Orchestration

πŸ“Š LLM Evaluation & Observability


πŸ† Certifications

πŸ… Dialogflow CX: Generative Playbooks β€” Google Cloud

πŸ… Conversational AI on Vertex AI and Dialogflow CX β€” Google Cloud

πŸ… Building a Virtual Agent with Dialogflow CX β€” Google Cloud

πŸ… LivePerson Certified Conversational Engineer

πŸ… Kore.ai Experience Optimization Platform Developer Advanced Training


🀝 Connect With Me

πŸ“§ Email: sindhuramu30@gmail.com

πŸ’Ό LinkedIn: linkedin.com/in/sindhu2406

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