AI Engineer | Microsoft MVP | Python Developer

I'm a Microsoft MVP and an AI engineer passionate about building agentic systems and democratizing AI. My expertise lies in designing multi-agent orchestration, integrating LLMs with real-world tools, and advancing the Model Context Protocol (MCP) ecosystem. I actively contribute to open-source, lead technical sessions on Azure AI, MCP, and agent frameworks, and help developers and organizations adopt responsible, production-grade AI solutions.

• Microsoft MVP : Azure AI Foundry
• Open Source Advocate & Community Speaker
• Builder of practical, agentic AI systems

I believe in democratizing AI, empowering developers through community skilling, and bridging industry with academia.

• Key contributor to MCP for Beginners — Authored hands-on labs and code samples for custom MCP server/client, and created the Advanced Topic lesson on Web Search MCP. My work focused on practical implementation, showing developers how to build, extend, and integrate custom MCP servers and clients for real-world AI agent interoperability.
• Key contributor to AI Agents for Beginners — Developed the Agentic RAG code sample (with its own repo), and authored the 11th MCP lesson featuring code for 3 agents and an MCP server. My contributions help learners understand and build agentic systems, multi-agent orchestration, and retrieval-augmented generation with real, production-grade code.
• Speaker & Mentor at Microsoft Reactor, Azure AI Foundry, and open-source events, helping hundreds of developers get started with AI agents and MCP.

• Languages: Python, Jupyter Notebook, Java
• AI/ML: Azure OpenAI, Semantic Kernel, AutoGen, scikit-learn, Hugging Face Transformers, TensorFlow/PyTorch, Kaggle pipelines, NumPy, Pandas, Matplotlib, Seaborn
• Agentic Frameworks: MCP (Model Context Protocol), FastMCP, Semantic Kernel Agents, Agentic RAG, AutoGen, LangChain, AI Agents Service, Azure AI Foundry
• Frameworks/Tools: FastAPI, Flask, Streamlit, Chainlit, Mediapipe, PyAutoGUI, OpenCV
• Cloud & APIs: Azure, Azure Cosmos DB, Azure Static Web Apps, Vercel, GitHub Pages, REST APIs
• DevOps: Docker, GitHub Actions, Vercel auto-deploy, GitHub Pages deployments, VS Code, FastAPI
• Databases: MongoDB, ChromaDB, Azure Cosmos DB (experimental)

• Tech Stack: FastAPI (Python 3.10+), MongoDB, Docker, Azure Container Apps, NVIDIA NIM API, Azure OpenAI API
• About: CodeNull is a full stack no-code website builder platform designed to help businesses and individuals create, deploy, and manage scalable websites without technical expertise. It integrates AI/LLM services for content generation, SEO, and chat-based assistance, and provides REST APIs for website generation, evaluation, and more.
• Problem Statement: Many small businesses, nonprofits, and SMEs lack the resources or skills to build and maintain modern websites. CodeNull solves this by offering a unified, cost-effective, and scalable no-code solution for website creation, content management, SEO, and deployment.
• Context & Impact:
  • Enables non-technical users to launch full stack websites quickly, reducing time-to-market and development costs.
  • Integrates advanced AI for content, SEO, and evaluation, making professional web presence accessible to all.
  • Supports containerized deployment and cloud scalability, used by early adopters in small business and freelance sectors.

• Tech Stack: Python, FastAPI, Chainlit, Azure OpenAI, Cosmos DB
• About: RoamMind is a travel AI assistant that leverages LLMs and multi-skill orchestration to help users plan trips, book services, and extract structured travel information. It manages conversation state and integrates with real APIs for flights, hotels, and more.

• Tech Stack: Jupyter Notebook, Python, Semantic Kernel, ChromaDB
• About: Agentic RAG demonstrates the use of Retrieval-Augmented Generation (RAG) with Semantic Kernel and ChromaDB. The project showcases how to build an AI agent that retrieves travel documents, augments user queries with semantic search, and streams detailed travel recommendations. It is designed to show real-world RAG applications, especially in the travel domain.
• Context & Impact:
  • Used in university workshops and referenced in open-source repos for LLM-based retrieval.
  • Bridges the gap between theory and practice for RAG, making advanced LLM techniques accessible to developers.
  • Features include ChromaDB integration for factual retrieval, semantic plugins, travel query support, and streaming chat history for context-aware conversations.

• Tech Stack: Jupyter Notebook, Python, Mediapipe, PyAutoGUI
• About: This project enables users to type on a virtual keyboard using finger gestures, powered by real-time hand tracking and automation. It is designed for accessibility and innovative UI/UX demos.

• GitHub: ShivamGoyal03
• LinkedIn: shivam2003
• Tech Blog: Microsoft Tech Community | Another Tech Blog Profile