Dillon Browne
Engineering resilient, scalable infrastructure for the AI era — from multi-cloud architecture to production ML pipelines.
"cloud": ["AWS", "Azure", "GCP"],
"devops": ["K8s", "Terraform", "ArgoCD"],
"ai_ml": ["LangChain", "RAG", "vLLM"],
"scale": "50TB+ daily traffic"
}
Core capabilities
Cloud & Platform
Multi-cloud architecture across AWS, Azure, GCP. Systems handling 50TB+ daily with 99.99% uptime.
AI/ML Infrastructure
Production LLM deployments, RAG systems, GPU orchestration. <100ms latency at scale.
DevOps & Automation
CI/CD pipelines, GitOps, IaC. 75% faster deployments across 100+ services.
Security Engineering
Zero-trust, DevSecOps, compliance automation. SOC 2 & ISO 27001.
Technologies
Impact & results
Infrastructure in motion
A live look at the path every workload takes — ingestion, processing, inference, output — across an observable GPU fleet, end to end.
ML Pipeline Flow
GPU Cluster
Real-time node status
Capabilities
Cloud & Infrastructure
- — Cloud migration strategy & execution
- — Multi-cloud architecture design
- — Disaster recovery planning
- — High-availability system design
DevOps & Automation
- — CI/CD pipeline architecture
- — Infrastructure as Code implementation
- — GitOps transformation
- — Development environment standardization
Kubernetes & Containers
- — Kubernetes cluster design & deployment
- — Container orchestration strategy
- — Service mesh implementation
- — Microservices migration
AI/ML Operations
- — LLM infrastructure setup
- — AI agent development & deployment
- — Vector database architecture
- — ML model serving pipelines
Observability & Reliability
- — Monitoring & alerting design
- — Performance optimization
- — Incident response automation
- — SRE practice implementation
Ready to start?
From consultation to full implementation, production-ready solutions tailored to your needs.
Frequently asked
What do you do?
I'm a Senior Cloud Architect and DevOps engineer with 10+ years building resilient, scalable infrastructure — multi-cloud architecture (AWS, Azure, GCP), Kubernetes, Infrastructure as Code, observability, security engineering, and increasingly production AI/ML infrastructure (RAG systems, LLM serving, GPU orchestration).
Are you available for work?
Yes. I take on full-time roles and contract engagements, remote or on-site, and typically respond within 24 hours. Use the contact form to start a conversation about your project or role.
What kind of AI infrastructure work do you do?
Production LLM deployments, retrieval-augmented generation (RAG) pipelines, GPU cluster orchestration, vector search, and AI-powered automation — with the same reliability, cost, and observability discipline applied to traditional cloud infrastructure.
How do you approach cost optimization?
Right-sizing, spot/committed-use strategy, autoscaling tuned to real demand, storage-tier and egress review, and continuous FinOps measurement. Past engagements have delivered roughly 40% average infrastructure cost reduction without sacrificing reliability.
How was this website built?
Edge-first: Astro static site with React islands, deployed on Cloudflare Workers with serverless functions, Workers AI for the chat assistant, and KV for rate limiting and caching. Read the full write-up in the "How I Built This" blog post.
What engagement models do you offer?
From advisory and architecture review through to hands-on implementation and operation — cloud migrations, Kubernetes platforms, CI/CD and GitOps, observability, and AI/infrastructure integration, scoped to your needs and outcomes.
Get in touch
Ready to discuss your next project? Fill out the form below and I'll get back to you within 24 hours.