Platform architecture
Multi-cloud platform design, migration planning, resilience, and translating enterprise constraints into practical technical decisions.
AWSAzureGCPOCI
Solutions architecture · DevOps · Cloud platforms
Infrastructure that is clear, resilient, and ready to scale.
I’m Mohamed Ayman, a solutions-focused infrastructure engineer with 10+ years of experience turning complex enterprise requirements into dependable cloud platforms and automation.
What I work on
From requirements to running platforms
I connect architecture, implementation, and operational reality—so the design works after the diagram is finished.
Infrastructure as code
Repeatable infrastructure and delivery workflows built around Terraform, Ansible, GitOps, and maintainable automation.
TerraformAnsibleGitOps
Containers and automation
Hands-on platform engineering across Kubernetes, OpenShift, AAP, execution environments, and modern CI/CD systems.
KubernetesOpenShiftAAP
Selected work
Architecture backed by implementation
Platform architecture
Multi-cloud Terraform Enterprise Platform
Architecture and automation for consistent Terraform Enterprise deployments across AWS, Azure, and Google Cloud.
Terraform EnterpriseTerraformAWSAzureGCP
Read project →Application delivery
GitOps Delivery for a Flask Portfolio on OpenShift
A tested Flask container delivered through GitHub Actions, Quay, and Argo CD, with a verified development release and automatic drift correction.
OpenShiftArgo CDGitHub ActionsQuayFlaskKustomize
Read project →OpenShift infrastructure lab
OpenShift Lab on a Hetzner Dedicated Server
A three-node OpenShift lab on one Hetzner dedicated server, deployed with Ansible, KVM/libvirt, and the Assisted Installer.
OpenShiftAnsibleKVM/libvirtHetznerAssisted Installer
Read project →Field notes
What I’m learning and building
AI Engineering Reading Notes — Chapter 3: Evaluation Methodologies
How do we know an AI application is good enough? Notes on measuring predictions, testing outcomes, comparing answers, and evaluating the judges themselves.
AI Engineering Reading Notes — Chapter 2: Understanding Foundation Models
Understanding what shapes a foundation model: the data it learns from, its architecture, how it is trained to answer, and how it selects output tokens.
AI Engineering Reading Notes — Chapter 1: Foundation Models
Reading Chapter 1 of AI Engineering: how language models learn, what makes a foundation model useful, and why adapting an existing model is a practical starting point.
Let’s connect
Working on a difficult infrastructure problem?
I’m always interested in thoughtful conversations about platform architecture, automation, and reliable delivery.
Start a conversation