DevOps

End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval.

Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work.

Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files.

Architect and provision enterprise Azure infrastructure from workload descriptions.

Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters.

Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd)…

List, find, and show Azure resources across subscriptions or resource groups.

Analyze Azure resource groups and generate detailed Mermaid architecture diagrams showing the relationships between individual resources.

Builds an Azure portal deep link to a specific blade/menu item for supported Microsoft.ContainerService, Microsoft.Kubernetes, and Microsoft.Compute resources…

Pre-deployment validation for Azure readiness.

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end.

Setup and diagnostics for the AWS DevOps Agent MCP connection.

Takes a finished eve branch from review to merge.

Create, build, deploy, and localize declarative agents for M365 Copilot and Teams.

Assess and migrate cross-cloud workloads to Azure with reports and code conversion.

Azure VM/VMSS router.

Plan, deploy, operate, and troubleshoot Azure Local (formerly Azure Stack HCI): sizing and prerequisites, Arc registration, lifecycle updates, workloads…

Plan, deploy, operate, and troubleshoot multi-rack (rack scale) deployments of Azure Local — preintegrated racks scaling to hundreds of machines, built on…

Deploy an Azure DocumentDB cluster (`Microsoft.DocumentDB/mongoClusters`) end-to-end — Bicep (primary), Azure CLI one-shot, Terraform, or portal.

Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux.

Push and publish custom AI models to Replicate, and set up CI/CD for releasing new model versions safely.

Complete end-to-end workflow: scaffold a new app and deploy it to Heroku.

Run Heroku plugin preflight checks.

Best practices for running Azure DocumentDB locally for development — choosing between the Gateway Docker image and the psql-only image, docker-compose setup…