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Hey there ๐Ÿ‘‹

I'm Afraz Ahmed, a Staff Engineer at DigitalOcean who spent a decade in infrastructure before realizing AI systems need people who actually understand production reliability.

What I'm Working On

Currently building AI systems that process 800+ customer conversations daily at Cloudways. The kind that actually ship to production and stay running.

Recent projects:

  • ๐Ÿค– AI QA Agent - Multi-agent system with >90% efficiency increase for our QA team. Redis queues, PII sanitization, the works.
  • ๐Ÿ› ๏ธ Cloudways Co-pilot - Natural language server management with a safety-first approach (because nobody wants AI accidentally nuking production)
  • ๐Ÿง  RAG Pipeline - RAPTOR-based knowledge system that doesn't hallucinate customer support answers
  • ๐Ÿ”— MCP Server - 43 tools covering the full Cloudways API with proper encryption and async architecture

The Tech Stack

AI/ML: Python, Google ADK Framework, Multi-Agent Systems, RAG Pipelines, FAISS, Model Context Protocol

Infrastructure Foundation: Kubernetes, Docker, CI/CD (GitHub Actions, ArgoCD), Linux, AWS, GCP, DigitalOcean

Services & Tools: Redis, MariaDB, Nginx, Apache, Varnish, ProxySQL, Terraform, Bash scripting that actually works

Philosophy

Most people talk about AI potential. I prefer shipping production systems that solve actual problems and stay running at 3 AM.

My infrastructure background means the AI systems I build don't just work in demosโ€”they handle real load, fail gracefully, and have proper monitoring. Because what's the point of intelligent automation if it needs constant babysitting?

Background

  • ๐Ÿ“ Based in Karachi, Pakistan
  • ๐Ÿ’ผ 15+ years: Started with Windows/Networking/Datacenter Management, evolved through Linux system management, Kubernetes orchestration, and now AI systems
  • ๐ŸŽฏ Current focus: Building multi-agent AI platforms that actually scale
  • ๐Ÿ“Š Results-driven: If it doesn't have measurable impact, I'm not interested

Let's Connect


Building AI systems that turn hype into measurable business outcomes. One production deployment at a time.

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