Cloud & DevOps / 2026
Enterprise-grade infrastructure built for performance, scalability, and reliability — automated delivery pipelines, containerized workloads, and hardened production environments, engineered as one coherent system.
Good infrastructure is invisible. It deploys itself, reports its own health, and fails without taking you down.
Every platform we run is built on four properties. Automated, so releases are repeatable and reviewable instead of heroic. Observable, so the system tells you what is wrong before your users do. Secure by default, so access is scoped, secrets are managed, and environments are isolated. Maintainable, so the team that inherits it can reason about it.
The practice covers the full path from a developer’s commit to a request served at the edge — CI/CD, containers and orchestration, CDN and cache architecture, server tuning, and the production discipline that keeps release day uneventful.
1# .github/workflows/deploy.yml2name: deploy3on:4 push: { branches: [main] }5jobs:6 release:7 runs-on: ubuntu-latest8 steps:9 - uses: actions/checkout@v410 - run: docker build -t app:${{ github.sha }} .11 - run: docker push registry/app:${{ github.sha }}12 - run: kubectl set image deploy/app app=registry/app:${{ github.sha }}13 - run: kubectl rollout status deploy/app # zero-downtime gate
Production workloads on AWS — compute, serverless, storage, DNS, and networking — alongside managed platforms and traditional VPS estates.
Production workloads on AWS — compute, serverless, storage, DNS, and networking — alongside managed platforms and traditional VPS estates.
Docker from local development to production: optimized multi-stage images, Compose environments, and Kubernetes deployments with rolling updates and horizontal scaling.
Delivery pipelines that make releases uneventful — automated builds, tests, and zero-downtime deployments across GitHub Actions, Bitbucket Pipelines, and GitLab CI.
Cache architecture across every tier — Cloudflare and Fastly at the edge, Varnish with custom VCL at the origin, and precise invalidation so content is fast without going stale.
Layered controls as the default posture: TLS everywhere, WAF and firewall rules, scoped access, isolated environments, and secrets kept out of code.
Nginx and Apache tuned for real traffic patterns, process management that survives restarts, and the unglamorous work — SSH, cron, Bash — done properly.
Logs you can query, errors with context, and performance profiles that point at the actual bottleneck — the difference between guessing and knowing in production.
Drupal in production at enterprise scale on Acquia, Pantheon, and Upsun (formerly Platform.sh) — deployment workflows, cache strategy, and platform-specific tuning.
Highly available topologies and multi-environment workflows — development, QA, staging, production — with promotion paths that keep every tier honest.
If it happens twice, it gets scripted. Provisioning, maintenance, and local Docker environments that make “works on my machine” mean the same thing everywhere.
Four live schematics — the delivery path, the cache hierarchy, release strategies, and cluster behavior under failure. These are the systems we build, drawn the way we reason about them.
Every change follows the same path: version control, automated build and test, an immutable image, a progressive rollout, and cache distribution. No manual steps, no snowflake releases.
A request should travel exactly as far as it must. Most terminate at the edge; only cache misses reach the application, and only real work reaches the database.
Deployments are a traffic decision, not an outage window. Blue/green switches whole environments with instant rollback; rolling replaces instances gradually behind the balancer.
Both environments run in parallel — cutover is a routing change, rollback is the same change reversed.
Capacity follows load and failure is routine: the balancer routes around unhealthy pods, the orchestrator replaces them, and the system returns to its declared state. Try it.
Desired state: 3 replicas. Kill a pod and the orchestrator converges back — no pager involved.
The patterns we are brought in for. Each starts with an audit of what exists and ends with a system the in-house team can operate without us.
A high-traffic Drupal platform outgrowing its origin: layered caching with Varnish and a CDN, tuned PHP and database tiers, and a clean multi-environment workflow on Acquia, Pantheon, or Upsun.
Replacing manual releases with pipelines: automated build, test, and deployment stages, environment promotion rules, and zero-downtime production rollouts in GitHub Actions, GitLab CI, or Bitbucket Pipelines.
An origin doing work the edge should absorb: a cache audit across browser, CDN, and application tiers, Cloudflare or Fastly configuration, Varnish VCL, compression, and correct invalidation.
Moving legacy hosts into containers: multi-stage images, parity between local and production, Compose-based development environments, and a registry-driven release flow.
From single-host Docker to an orchestrated cluster: templated workloads, rolling deployments, horizontal scaling, and health-check-driven self-healing.
Lifting an aging estate into AWS or a managed platform: VPC and IAM design, environment isolation, automated provisioning, and a staged cutover plan that protects uptime.
Environments are defined in version control and reviewed like any other change. If it can’t be recreated from the repository, it doesn’t exist.
Build once, promote the same artifact through every environment. Servers are replaced, not patched in place.
Any step a human repeats is a step that will eventually be done wrong. Pipelines, provisioning, and maintenance are scripted by default.
Least privilege, isolated environments, managed secrets, and encrypted transport are the starting point — not a hardening phase at the end.
A system that can’t explain its own behavior isn’t finished. Logs, metrics, and error context ship with the first release.
Latency is a design constraint. Cache placement, payload budgets, and origin efficiency are decided up front, not retrofitted.
Scale horizontally, keep state at the edges of the design, and let capacity follow demand instead of guessing at it.
Deployments are routine, rollbacks are instant, and failure is an expected input the system is designed to absorb.
