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arindam-giri/README.md

I believe, the most powerful solutions emerge at the intersection of diverse - and often unrelated - disciplines.

I specialize in designing and implementing enterprise-grade Generative AI, Agentic AI solutions using commercial Large Language Models and modern agentic frameworks. Currently, I am focused on scaling AI agents for production - building reliable, high-performance agentic systems that can operate effectively in real-world enterprise environments with real users.

Currently pursuing: Scaling AI Agents for production, distributed system design, Claude Code Agent SDK, Microsoft Agent Framework, MCP protocols, Agent to Agent (A2A) protocol, multi-agent collaboration, Agent Telemetry.

Recent works:

Multi-Agent Platform for Enterprise process automation: Architected and delivered a production-grade multi-agent conversational AI paltform with hierarchical Orchestrator topology with runtime skill discovery, MCP integration, Redis-backed memory, SSE, and OpenTelemetry tracing. Built enterprise guardrails via a multi-layer middleware pipeline with content filtering and HITL workflows. Uses LangGraph, LangChain, Agent Skills and Middleware, FastAPI, Python, Redis, vLLM, and OpenTelemetry.

GenAI-powered, multi-cloud incident management platform that ingests and validates alerts from diverse sources, leveraging dynamic Pydantic schemas and scalable architecture principles. Integrated Microsoft Agent Framework, Azure AI Search, and OpenAI models to enable intelligent decision-making for automated incident creation, resolution, and relavant ITSM workflows.

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  1. core-ML-works core-ML-works Public

    Jupyter Notebook

  2. excel-agent-project-llm excel-agent-project-llm Public

    A complex agentic design and LLM engineering solution to do RAG on complex large excels having tables, unstructured comments, pivots etc without vector. [In progress]

    Python

  3. image-segmentation-test image-segmentation-test Public

    Python