Senior DevOps Engineer & Cloud Architect
I'm Daniel Ulisses, a Senior DevOps Engineer and Cloud Architect from Brazil with 15+ years in IT. I design and automate platforms on Azure, AWS and GCP with Terraform, Kubernetes and GitOps, and lately I'm deep into AI engineering for DevOps. This is my corner of the internet: my resume, selected work and my notes.
Tools I use
What I work on
Fifteen years of infrastructure, from VMware and Exchange to Terraform, AKS and AI tooling. The common thread: automating things so teams can trust and understand them.
Azure-first but fluent in AWS and GCP: landing zones, networking, identity, migrations and data platforms on Microsoft Fabric and Databricks.
Terraform, Terragrunt and Terratest, Azure DevOps and GitHub Actions pipelines, AKS delivered with GitOps and Argo CD.
Enterprise AI rollouts like Gemini Enterprise, plus LLMs and agents for IaC review, incident triage and runbook automation, with proper guardrails.
Clear documentation, presentations and hands-on sessions, so the teams I work with can own what we build.
15+ years in IT
Still hands-on every week
About me
I'm a Senior DevOps Engineer and Cloud Architect with 15+ years in IT. I started out racking servers, running VMware datacenters and keeping Exchange alive, and today I design and automate cloud platforms on Azure, AWS and Google Cloud.
Most of my work sits where architecture meets automation: Infrastructure as Code with Terraform and Terragrunt, CI/CD with Azure DevOps and GitHub Actions, and Kubernetes (mostly AKS) delivered through GitOps with Argo CD. I've worked on cloud migrations, enterprise data platforms, greenfield Kubernetes builds and a lot of "let's bring this brownfield estate under IaC" projects.
Current projects
The projects taking up most of my time at the moment.
Rolling out Google Gemini Enterprise across a large organisation: platform setup, identity and access, data connectors, governance and security guardrails, and enablement for the people using it.
Administering Kubernetes applications end to end: build and test pipelines, GitOps-driven deployments, environment promotion, and day-2 operations.
Implementing data platforms on Microsoft Fabric and Databricks, with infrastructure as code, automated deployments and CI/CD for data workloads.
Selected work
A few past projects I'm proud of. Client names are kept private.
Designed and implemented an enterprise data platform spanning Azure and GCP for a global banking company, fully provisioned with IaC and delivered through automated pipelines.
~80% better data quality · ~70% stronger data security
Containerised a microservices web application and deployed it to Azure Kubernetes Service, with Azure DevOps for CI and Argo CD for continuous delivery.
Higher scalability, much shorter deployment times
Experiments, modules, scripts and the occasional half-finished idea.
From the blog
Practical write-ups on cloud architecture, GitOps and AI engineering, mostly things I had to figure out the hard way.
I work on several projects at once. This is the harness loop I built around my coding agents (worktrees, tickets as a dependency graph, and a cleanup step), and how it evolved from a shell alias into something I trust.
What I set up (and agree on with the team) before anyone installs Argo CD. Spoiler: most of it is conventions, not tools.
Everyone wants an AI agent in their pipeline. Here's where I've seen them pay off, where they cause trouble, and the setup I suggest to keep them out of production.
Contact
Questions about a post, want to swap notes on platform engineering or AI tooling, or just want to say hi? My inbox is open, and I read everything myself.
Based in
Brazil