SheetProfile

About Me

Ten years of cloud platforms for regulated enterprises. Now building the guardrails that let AI operate them.

Experience: 10+ yrs
Clouds: AWS · Azure
Focus: AI-native ops

I build cloud platforms for organizations that cannot afford a bad deploy.

I'm Hammad Haqqani, a DevOps Architect. For more than ten years my work has been inside banks, mortgage servicers and federal agencies: Capital One, Mr. Cooper Group, the US Department of Commerce and the US Department of Veterans Affairs. In those places every change is audited, every outage has a cost, and nobody signs off on “move fast and break things.”

Note 01What regulated work taught me

Speed comes from safety, not the other way round.

Teams ship slowly when they are afraid of their own pipeline. Take the fear away, with infrastructure defined in code, policy checks that run on every change, and rollbacks nobody has to think about, and the speed follows. On one of those teams, a deployment cycle that took two hours came down to minutes. Nothing about that was clever. It was the boring work done properly, which is most of what good infrastructure is.

Note 02Then the tools changed

An agent can write a thousand lines of Terraform in a minute.

Whether those lines should reach production is the same question it always was, and it is the one I have spent a decade learning to answer. I now do my infrastructure work alongside Claude Code, and the output is faster than anything I could type. The judgment about what to ship has not moved to the machine.

AIdoesn'tremovetheneedforengineeringjudgment.Itraisesthepriceofnothavingit.

So my focus today is the part most teams skip: the guardrails that make AI usable in serious environments. Review gates on every generated change. Policy as code. Agents with narrow permissions. Runbooks an LLM can search and a human can trust. People who stay accountable for what ships.

Note 03What I do now

I design platforms, advise teams, and build in the open.

I work as a DevOps Architect, help engineering teams adopt Claude Code without breaking prod, and publish what I learn. This site carries 118 free tools and a blog where I write down what worked and what broke.

If you are bringing AI into a platform that can't afford mistakes, I'd like to hear about it.

Get in touch See the work Consulting

What I'm useful for.

Six things teams bring me in to fix, in the order they usually come up.

Platforms that pass an audit

Cloud architecture on AWS and Azure for environments where compliance is part of the design, not a review bolted on at the end.

Pipelines people trust

CI/CD with policy checks on every change, so a release is routine work on a Tuesday instead of an event everyone braces for.

AI inside the engineering loop

Claude Code, Codex and agents doing real infrastructure work, behind review gates and narrow permissions.

Operations that explain themselves

Monitoring that uses LLMs to read the telemetry and say what is wrong in plain English, with the runbook attached.

Spend you can explain

Cost analysis that shows where the money goes and what to rightsize, before finance has to ask.

Teams that keep it running

Mentoring engineers on working with AI copilots, so the practice outlasts my involvement.

How I actually work

Daily tools

Claude CodeCodex CLIClaude APICursor AIGitHub Copilot

Claude Code is my main pair-programmer for infrastructure: Terraform modules, Kubernetes manifests, CI/CD pipelines. It drafts faster than I can type. Nothing it writes is applied until it has been read.

Patterns I build

RAG PipelinesLangChainAIOpsVector DBsPrompt Engineering

RAG systems that search internal documentation and CloudWatch logs during an incident, so the engineer on call gets an answer and where it came from. AIOps that reads the metrics and proposes a remediation plan for a person to approve.

Philosophy

The best infrastructure is the kind nobody talks about.

Nobody praises a platform on the day nothing goes wrong, and that day is the goal. I treat LLMs as working parts of the stack, not a demo: a Terraform plan gets a second read from Claude, an alert arrives with context from an agent, a deployment is tested before it is trusted. If the result is boring, it worked.

Personal

Away from the keyboard, I walk uphill.

I hike and I read, and both turn out to be good training for this job: they reward patience and punish shortcuts. The reading list is mostly AI research papers these days. I also write about AI-powered DevOps and mentor newer cloud engineers, because explaining something is the fastest way to find out whether I understand it.