Vaibhav Anand

About Vaibhav Anand

I build intelligent AI systems that solve real-world automation problems from multi-agent test automation platforms to RAG-powered chatbots and computer vision applications.At HCL Technologies, I\'ve shipped 5 production-ready AI POCs in 2 years- Multi-Agent Test Automation System: Automated the entire software testing lifecycle using Microsoft Agent Framework. From requirement extraction (Azure DevOps APIs) → test case generation (GPT-4) → execution framework integration. Currently in development- Multi-Agent Sentiment Analysis Platform: Built distributed system orchestrating Python agents (AutoGen) and.NET microservices, processing 10K+ reviews across YouTube, Twitter, Facebook, IMDb with VADER, TextBlob, and Azure OpenAI- AI-Powered Beauty Platform: Engineered 3-stage image generation pipeline using Azure Functions, GPT-4o for prompt engineering, and GPT-Image1 for photorealistic makeup visualization- E-Commerce RAG Chatbot: Architected semantic search system handling 10K+ product FAQs using Azure Cognitive Search + OpenAI, with 3-turn conversation memory- Agentic RAG System: Built FastAPI-based retrieval system with hybrid search (BM25 + vector similarity), ChromaDB, FlashRank reranking, and Google Gemini integration- Flask Blog Platform: Full-stack web app with user auth, CKEditor, comment system, and Gravatar integration.I specialize in: Multi-agent orchestration (AutoGen), RAG architectures, Azure OpenAI integration, computer vision (OpenCV, Mediapipe), and bridging AI prototypes with production-ready systems.Currently exploring: Agentic workflows, distributed AI systems, and LLM-powered test automation.Tech stack: Python, Azure OpenAI, AutoGen, LangChain, FastAPI.NET, Azure Functions, ChromaDB, FAISS, Selenium, Docker.Open to: AI Engineer roles, ML Engineering positions, and consulting on multi-agent AI systems.

Experience

1. Ai Engineer

   HCLTech

   Feb 2024 — Present · Lucknow, IN

   Build AI-powered automation systems using Azure cloud services, specializing in multi-agent orchestration, RAG architectures, and computer vision applications- Multi-Agent Test Automation System (In Development)Building an automated testing platform using Microsoft Agent Framework that extracts work items from Azure DevOps and generates executable test cases with GPT-4- Multi-Agent Sentiment Analysis PlatformDeveloped distributed system coordinating Python agents (AutoGen) and.NET microservices to analyze reviews across multiple platforms. Integrated VADER, TextBlob, and Azure OpenAI with automated scheduling and Azure Table Storage- AI Beauty Personalization PlatformCreated serverless application using Azure Functions with three-stage pipeline: GPT-4o prompt generation, GPT-Image1 image creation, and Computer Vision-based personalization with Azure Blob Storage integration- E-Commerce RAG ChatbotBuilt semantic search chatbot handling product FAQs using Azure Cognitive Search and Azure OpenAI with conversation memory and Gradio interface- Agentic RAG SystemDeveloped FastAPI-based RAG system with hybrid retrieval (BM25 + vector search), FlashRank reranking, and Google Gemini integration. Supports multiple document formats with ChromaDB persistence- Trained 50+ team members on LLM output validation methodologies for AI-generated test cases.

Education

• Amity University, Noida

  Bachelor of Computer Application-BCA, Computer Science