AI-augmented cloud architect and DevOps strategist building the next generation of intelligent infrastructure.
I am Hammad Haqqani, a DevOps Architect, Cloud Solutions Strategist, and AI infrastructure engineer. Over the past decade, I've evolved from traditional cloud engineering into the frontier of AI-native infrastructure, where large language models like Claude and Codex are integral to how I design, deploy, and operate cloud systems.
I've led cloud initiatives at Capital One, Mr. Cooper Group, the US Department of Commerce, and the US Department of Veterans Affairs. Today, my focus is on embedding AI intelligence directly into the infrastructure layer, using Claude Code to generate Terraform at scale, building RAG-powered knowledge bases for operational runbooks, and deploying AI agents that manage cloud environments.
I believe the future of DevOps is AI-augmented: where engineers work alongside AI copilots to write infrastructure code, where monitoring systems use LLMs to explain anomalies in plain English, and where incident response is orchestrated by intelligent agents.
Using Claude Code, Codex, and LLM agents to automate infrastructure provisioning, incident response, and cloud operations at enterprise scale.
Deploying AI and cloud solutions that align with business goals, from intelligent autoscaling to predictive cost management.
Mentoring teams on integrating AI copilots into DevOps workflows, fostering cultures that embrace continuous improvement and intelligent automation.
Designing robust, AI-augmented cloud solutions across AWS and Azure that ensure scalability, reliability, and self-healing capabilities.
Building intelligent monitoring systems that use LLMs to analyze telemetry, predict failures, and auto-generate incident runbooks from real-time data.
AI Copilots & Agents
I use Claude Code as my primary AI pair-programmer for all infrastructure work, from writing Terraform modules to debugging Kubernetes manifests to generating CI/CD pipelines.
AI Infrastructure Patterns
I architect RAG systems that query internal documentation and CloudWatch logs to power intelligent incident response. My AIOps implementations use LLMs to analyze metrics and generate remediation plans.
Philosophy
The best infrastructure is invisible. It thinks, adapts, and heals itself. My approach fuses deep cloud engineering expertise with AI-first thinking: using LLMs not as novelties, but as core primitives in the infrastructure stack. Every Terraform plan reviewed by Claude, every alert enriched by an AI agent, every deployment validated by intelligent testing.
Personal
Away from the keyboard, I am an avid hiker and reader, finding as much joy in scaling mountains as I do diving into the latest AI research papers. I'm also passionate about sharing knowledge, writing about AI-powered DevOps and mentoring the next generation of cloud engineers.