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Achievements

Achievement: Pull SharkAchievement: Arctic Code Vault Contributor

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ahmedbutt2015/README.md

Hi 👋, I'm Ahmed Fayyaz

Typing SVG

I build scalable web applications and AI-powered systems (RAG, agents, automation) used in real-world products.

🚀 Open to Opportunities

  • 🌍 Open to Remote (Global) roles
  • 💼 Looking for Full Stack / AI Engineer roles
  • ⚡ Interested in AI products, SaaS, and startup environments
  • 📫 Reach me at: imahmedthebutt@gmail.com

🧠 What I Bring

  • 5+ years building production-grade full-stack applications
  • Strong experience with Laravel, Node.js, Vue/Nuxt, TypeScript
  • Hands-on work with LLMs, RAG pipelines, embeddings, semantic search
  • Experience building AI agents & multi-step workflows
  • Solid backend fundamentals: APIs, queues, Redis, system design

🔥 Highlight Project

GraphOS — Service Mesh for AI Agents
👉 https://github.com/ahmedbutt2015/graphos

  • Open-source observability and policy layer for LangGraph agents — TypeScript and Python at full feature parity
  • One-line wrap → LoopGuard, BudgetGuard, MCPGuard policies, plus a built-in OpenAI / Anthropic price table
  • Local-first React Flow dashboard with time-travel replay over SQLite — zero SaaS, zero telemetry leaving the machine
  • Shipped on npm (@graphos-io/sdk) and PyPI (graphos-io); validated end-to-end by wrapping real open-source LangGraph benchmarks (agents-from-scratch-ts, retrieval-agent-template)

💼 Other Notable Work

WIZniche — CRM Platform (Gen AI features)

  • Worked on WIZniche, a production CRM, where I designed and shipped the Gen AI feature set into the existing product
  • LLM-assisted workflows, content generation, and smart insights integrated directly into the CRM flow without disrupting day-to-day usage
  • Brought together prompt design, retrieval, and structured-output validation so the AI features held up under real customer data

🛠️ Tech Stack

Laravel Node.js Vue.js Nuxt.js TypeScript React Next.js Express.js OpenAI Redis MySQL MongoDB Docker

💡 What I’m Good At

  • Turning ideas → production-ready systems
  • Building scalable APIs & backend architecture
  • Shipping AI features that actually work in real use cases
  • Working in fast-paced startup environments

🤝 Let’s Connect

⚡ Quick Note

I prefer working in environments where I can take ownership, ship fast, and solve real problems — especially where AI meets real-world products.

Profile views

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  1. spine spine Public

    Generate a personalized reading tour and verified architecture spine for any codebase.

    TypeScript 8 1

  2. graphos graphos Public

    GraphOS is an open-source governance and observability layer designed specifically for LangGraph and the Model Context Protocol (MCP).

    TypeScript 3

  3. agentic-docs-ai agentic-docs-ai Public

    Self-hosted compliance triage and document chat with FastAPI, React, pgvector, and LangGraph

    Python

  4. deal-agent deal-agent Public

    Drop in deal documents → get an onboarding plan, draft invoice, and stakeholder summary. Multi-agent LangGraph pipeline with RAG, human approval, and self-correcting retries.

    Python 1

  5. LangValidator LangValidator Public

    Self validating LangGraph agent pipeline: every agent output passes through a checkpoint node that scores it and routes to pass, retry with feedback, or halt. Includes rule based, LLM as judge, and…

    Python 1

  6. langflow-ai/openrag langflow-ai/openrag Public

    OpenRAG is a comprehensive, single package Retrieval-Augmented Generation platform built on Langflow, Docling, and Opensearch.

    Python 4k 396