Building intelligent AI applications powered by Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), and modern Python backends to automate workflows, enhance knowledge discovery, and support intelligent decision-making.

I'm an AI Engineer passionate about building intelligent software that combines Large Language Models (LLMs), Agentic AI, Multi-Agent Systems, Retrieval-Augmented Generation (RAG), and Machine Learning to solve real-world problems. I enjoy exploring how modern AI can transform traditional software into systems capable of reasoning, retrieving knowledge, automating workflows, and supporting better decision-making.

My work focuses on developing practical, production-inspired AI applications across domains such as document intelligence, semantic search, enterprise knowledge management, computer vision, workflow automation, technical interview platforms, and intelligent business solutions. Every project is designed to strengthen my understanding of scalable AI engineering while applying modern software development practices.

I enjoy working across the complete AI application stack—from integrating foundation models and building Retrieval-Augmented Generation (RAG) pipelines to developing FastAPI backends, designing REST APIs, implementing vector databases, orchestrating AI workflows with LangChain and LangGraph, and deploying applications using Docker and cloud-native technologies.

I hold a Master of Computer Applications (MCA) with a specialization in Artificial Intelligence & Machine Learning. I'm continuously expanding my knowledge in LLMOps, Model Context Protocol (MCP), AI evaluation, multi-agent collaboration, and enterprise AI architectures, while building projects that demonstrate practical engineering skills and modern AI development practices.

I believe the most impactful AI solutions combine strong software engineering principles with responsible AI development, creating applications that are scalable, maintainable, and capable of delivering meaningful value in real-world environments.

A modular multi-agent orchestration framework designed to demonstrate intelligent workflow planning, reasoning, tool execution, and persistent memory management. The platform coordinates specialized AI agents to collaboratively solve complex business tasks through structured workflows and autonomous decision-making.

Key Highlights

• Multi-Agent Collaboration
• Workflow Orchestration
• Tool Calling
• Stateful Memory
• Modular Agent Architecture

Tech Stack: LangGraph • FastAPI • Python • Google Gemini • Agentic AI

Repository:
https://github.com/imarpitajaiswal/eaios-orchestration-engine

A multi-agent AI system that simulates ERP reconciliation by autonomously identifying transaction discrepancies, analyzing exceptions, and generating actionable recommendations. The project demonstrates how intelligent agents can automate repetitive financial and operational workflows.

Key Highlights

• AI-driven Exception Analysis
• Multi-Agent Workflow Engine
• Financial Data Reconciliation
• Autonomous Decision Support
• RESTful Backend APIs

Tech Stack: LangGraph • FastAPI • PostgreSQL • Python

Repository:
https://github.com/imarpitajaiswal/enterprise-erp-reconciliation-swarm

An AI-powered document processing platform that combines OCR, Retrieval-Augmented Generation (RAG), and Large Language Models to extract, understand, and retrieve information from business documents. Designed to simplify enterprise knowledge discovery through natural language interactions.

Key Highlights

• OCR-based Document Processing
• Intelligent Information Extraction
• Semantic Search
• Retrieval-Augmented Generation (RAG)
• AI-powered Question Answering

Tech Stack: OCR • Gemini • LangChain • FastAPI • Vector Search

Repository:
https://github.com/imarpitajaiswal/enterprise-doc-intelligence

A multilingual semantic search engine enabling users to retrieve relevant information across documents written in different languages using embeddings, vector search, and natural language understanding. Designed to improve global knowledge accessibility.

Key Highlights

• Cross-Language Information Retrieval
• Semantic Search
• Vector Embeddings
• Multilingual NLP
• Enterprise Knowledge Search

Tech Stack: Python • NLP • Embeddings • Vector Search • LLMs

Repository:
https://github.com/imarpitajaiswal/enterprise-multilingual-search

An AI-powered logistics intelligence platform that models supply chain planning, shipment workflows, and operational decision support. The project demonstrates how AI can optimize logistics processes through intelligent automation and scalable system design.

Key Highlights

• Supply Chain Intelligence
• Workflow Automation
• Route & Process Optimization
• Decision Support
• Scalable AI Architecture

Tech Stack: Python • FastAPI • AI Workflows • Machine Learning

Repository:
https://github.com/imarpitajaiswal/sovereign-logistics-architecture

A real-time computer vision application that performs object detection and edge analytics using YOLO and OpenCV. Built to demonstrate low-latency AI inference on edge devices with an interactive dashboard for monitoring detections.

Key Highlights

• Real-time Object Detection
• Edge AI Inference
• Computer Vision
• Interactive Dashboard
• Live Video Analytics

Tech Stack: YOLOv8 • OpenCV • Streamlit • Python

Repository:
https://github.com/imarpitajaiswal/iot-edge-analytics

I'm continuously expanding my expertise in enterprise AI systems and modern LLM engineering, with a strong emphasis on building production-ready applications.

• 🤖 Designing Multi-Agent AI Systems using LangGraph and Agentic AI workflows

• 📚 Building Retrieval-Augmented Generation (RAG) applications for enterprise knowledge retrieval

• 🔗 Exploring the Model Context Protocol (MCP) ecosystem for interoperable AI agents

• ⚙️ Implementing LLMOps practices for deployment, monitoring, and evaluation

• 📄 Developing Document Intelligence solutions powered by OCR, semantic search, and Large Language Models

• 🌍 Building Multilingual AI applications using embeddings and vector search

• ☁️ Creating scalable AI backends with FastAPI, Docker, and cloud-native development practices

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Building practical AI applications using Generative AI, Machine Learning, and modern software engineering.