Microsoft MVP in Artificial Intelligence

Open-Source & Community Leadership

Active open-source contributor, technical community leader, and academic mentor across Microsoft AI ecosystems.

• ▸ Discord Community Lead: Led moderation and technical enablement for 32,000+ member Microsoft Foundry community
• ▸ Microsoft Open-Source Contributor: Contributed Model Context Protocol (MCP) implementations and AI agent samples to Microsoft GitHub repos
• ▸ Academic Mentorship – Imperial College CampusSphere: Mentored students to build a multi-agent AI system with 30+ custom MCP tools; featured on Microsoft Tech Community

Public Speaking & Technical Education

Delivered technical talks, live webinars, and workshops on Agentic AI, Microsoft Foundry, and multi-agent orchestration at Microsoft Reactor and global events.

• ▸ Microsoft Reactor – Learn Live: Live-coded multi-agent orchestration with Semantic Kernel for 5,400+ viewers
• ▸ Azure AI Influencers Day, Gurugram: Featured speaker and session lead for 400+ developers
• ▸ AI Mastery Series – Microsoft Foundry Webinar: Demonstrated Model Context Protocol (MCP) to 1,000+ learners across 3 days
• ▸ Microsoft AI Skills Challenge: Expert technical moderator for Microsoft Learn Live global assessment program

MVP Activity Autopilot

An automation system using Playwright MCP that reduces MVP submission time by 80%. Features snapshot-based error recovery and audit-friendly logging.

Imperial College CampusSphere

Mentored students to build a multi-agent campus management prototype using 30+ custom MCP tools. Featured on Microsoft Tech Community.

AI Agents & Agentic Systems

Deep hands-on experience building production-ready agent architectures using Azure OpenAI, Microsoft Agent Framework, Semantic Kernel, and LangChain - validated through projects like RoamMind, MediMind, and Agentic RAG implementations.

• ▸Multi-tool orchestration & agentic RAG pipelines
• ▸Memory handling, context management & stateful agents
• ▸LLMOps lifecycle - evaluation, monitoring & reliability
• ▸AI Agents blog series: 25K+ views across 10 parts

Applied Generative AI on Azure

Practical expertise in Azure AI Foundry (Microsoft Foundry), Azure OpenAI Service, and Model Context Protocol (MCP) - from prompt engineering and responsible AI guardrails to containerized production deployments using azd and Docker.

• ▸Microsoft Foundry & Azure AI Studio end-to-end delivery
• ▸Azure OpenAI integrations - GPT-4o, embeddings, function calling
• ▸Responsible AI implementation & ethical safeguards
• ▸Contributor to Microsoft's official AI learning repositories

Technical Writing & Developer Education

Published AI engineering content reaching 89,599+ views on Microsoft Tech Community - designing structured learning paths, step-by-step tutorials, and open-source enablement programs that position technical concepts accessibly for developers at all levels.

• ▸Structured beginner-to-intermediate AI learning series
• ▸Live webinars, Microsoft Reactor sessions & workshops
• ▸Community mentorship & open-source code reviews

Cloud-Native AI & DevOps

Experience shipping infrastructure-ready AI solutions with modern DevOps practices - CI/CD pipelines, Docker-based agent deployments, and automated observability workflows designed for reliable, scalable AI delivery on Azure.

• ▸CI/CD with GitHub Actions for AI workloads
• ▸Docker-based containerization & Azure deployment pipelines
• ▸Automated stats & documentation pipelines

Full-Stack AI Product Engineering

End-to-end AI product development from system architecture to UX - shipping startup-grade solutions with Python, FastAPI, Streamlit, and Chainlit. Accepted into Microsoft for Startups Founders Hub, demonstrating product thinking beyond engineering.

• ▸Accessibility-first, UX-focused AI tool design
• ▸Startup-grade AI solution architecture (CodeNull, LexiVerse)
• ▸API design, integration & deployment-ready packaging

Machine Learning Foundations

Academic and research grounding in core ML disciplines - providing principled reasoning behind practical AI engineering decisions, evaluation strategies, and model selection for production systems.

• ▸Reinforcement learning & optimization techniques
• ▸Medical ML prediction model development
• ▸Core ML algorithms applied to real-world AI engineering