Pankaj Meena

About Pankaj Meena

Full Stack Engineer with 3+ years of experience building scalable web applications and AI-powered automation systems.I specialize in designing production-grade systems that combine:• MERN stack (React, Node.js, MongoDB)• Microservices & event-driven architecture• Retrieval-Augmented Generation (RAG) pipelines• Vector databases (Milvus) & semantic search• LLM integrations (Gemini API)• Dockerized distributed servicesAt DataNnovite Sol, I architected and delivered an end-to-end AI-driven document intelligence platform integrating LLMs, vector search (Milvus), and distributed backend services for structured and unstructured document processing.I enjoy solving real-world automation problems by combining scalable backend systems with AI-driven intelligence.Currently exploring advanced system design, scalable AI infrastructure, and production-ready GenAI architectures.

Experience

1. Software Engineer

   Datannovite Sol

   Jun 2023 — Present · Pune, IN

   DataNnovite is an AI-driven intelligent document processing platform that can extract data from structured and unstructured documents like Invoices, and KYC documents in image & pdf format.2. Built and deployed a production-grade Progressive Web App (PWA) using React.js, enabling seamless document uploads and real-time processing.3. Utilized Firebase Storage for storing uploaded files, improving data management.4. Designed and developed RESTful APIs using Node.js and Express.js, supporting scalable backend architecture and secure authentication mechanisms (JWT-based).5. Designed microservices using event-driven architecture with RabbitMQ to enable asynchronous processing between distributed services.6. Containerized backend services using Docker to support production deployment and scalable distributed systems.7. Utilized LLM-powered Generative AI (Gemini API) for data extraction and transformation into various formats including key-value, table, and text.8. Designed and implemented a production-grade RAG pipeline using LangChain and Milvus vector database to enable semantic search and context-aware AI responses.9. Implemented Server-Sent Events (SSE) in Node.js to enable real-time responses to user queries.10. Tech Stack: LLM (Gemini API), Generative AI, Python, Flask, Docker, Milvus, MongoDB, Firebase, Node JS, RabbitMQ, React JS, JavaScript, CSS, HTML, Bootstrap.

Education

• Newton School

  Computer Science

• Indian Institute of Technology, Roorkee

  Bachelor of Technology, Metallurgical and Materials Engineering