One senior consultant, accountable end to end.
I'm Vitale Mazo. Organizations bring me in through Mazo Cloud Group LLC for surgical cloud and AI infrastructure projects across AWS, Azure, and GCP — a landing zone, a GPU platform, a zero-trust rollout — that need senior expertise now, without an agency bench or a six-month hiring cycle. I scope the work, I build it, and I hand it off running.
When you need the expertise, not the headcount.
Some projects don't need a consultancy or a new hire — they need one proven senior engineer for a defined piece of work. That's the entire model here.
Days, not a hiring quarter. From first call to a signed scope in about a week — no sourcing, no screening pipeline, no bench politics. You evaluate one person, once.
The person who scopes your project is the person who writes the Terraform, tunes the cluster, and stands behind it in production. Nothing is delegated downstream, because there is no downstream.
Project, retainer, or contract terms through a Texas LLC. No agency markup, no permanent overhead, and the corp-to-corp paperwork your procurement team already knows.
Deep capabilities. One engineer accountable for all of them.
These are the areas where I go deepest. If your project spans several of them, that's the point — one person can hold the whole picture, from the network core to the agent layer.
Cloud Platform Engineering
Multi-cloud foundations — AWS, Azure & GCP — that scale developer teams safely.
- Landing zones — AWS Control Tower & Account Factory for Terraform, Azure management groups & policy
- Multi-account governance, SCP guardrails & GitOps account vending at scale
- Golden-path Terraform & CloudFormation modules, Service Catalog self-service
- Platform standards that cut configuration drift across environments
Global & Hybrid Networking
Enterprise networks spanning regions, clouds, and data centers.
- AWS Cloud WAN core networks — policy as code, segmented routing, multi-Region edges
- Service insertion with centralized inspection & egress (AWS Network Firewall)
- Hybrid & cross-cloud connectivity, hub-and-spoke & DNS architecture
- Multi-region DR & resilience design
AI & GPU Infrastructure
Production inference and training platforms, not slideware.
- Kubernetes GPU platforms — NVIDIA GPU Operator, MIG & time-slicing on EKS, AKS & GKE
- vLLM self-hosted model serving; Bedrock, SageMaker, Azure ML & Vertex AI integration
- Karpenter autoscaling tuned for GPU economics
- Capacity, scheduling & cost governance for GPU fleets
Enterprise AI & Agentic Platforms
The layer most teams get stuck on — agents that survive contact with production.
- Multi-agent orchestration & MCP tool servers
- RAG pipelines — vector databases, knowledge graphs, session & long-term memory
- LLM gateways — routing, authentication & cost-control layers in front of model fleets
- Guardrails, evals & observability for agent fleets
DevOps, CI/CD & GitOps
Paved roads that make the secure path the easy path.
- Pipelines — GitHub Actions, GitLab CI, CodeBuild & CodePipeline, Jenkins
- GitOps delivery with ArgoCD — app-of-apps, drift-free environments
- DevSecOps — shift-left scanning, SRE observability baselines
- Developer experience — self-service templates & inner-source modules
Data & ML Pipelines
The data side of AI — the layer your agents are only as good as.
- AI/ML pipelines on SageMaker, Azure ML & Vertex AI
- Advanced data ingestion & analytics platform enablement
- Containerized data services on Kubernetes
- Regulated-data delivery — SOC 2, HIPAA, PCI DSS & FedRAMP-aligned
Zero-Trust & Network Security
Identity-centric access for regulated enterprises.
- Zscaler ZIA / ZPA architecture & operations
- Microsoft Entra ID integration, conditional access & cross-cloud identity federation
- Segmentation, inspection & egress control design
- DLP and SSL-inspection programs that hold up to audit
Architecture Advisory & Enablement
A fractional principal engineer for decisions that outlive the quarter.
- Platform & AI-readiness assessments with prioritized roadmaps
- Design reviews, vendor and reference-architecture evaluation
- Standards, guardrails & IaC strategy for platform teams
- Pairing and upskilling alongside your senior engineers
I work every layer — and the seams between teams.
Enterprise AI programs rarely fail inside a single layer. They fail in the handoffs between the teams that own them. I design and implement the whole stack, so the integration points are engineered instead of negotiated.
Agent applications & experience
usually owned by: product & app teamsOrchestration & execution
usually owned by: platform + app teamsKnowledge, memory & guardrails
usually owned by: data + ML teamsModel hub
usually owned by: ML engineeringGPU & compute platform
usually owned by: platform engineeringCloud & network foundation
usually owned by: cloud infra + securityEach layer typically belongs to a different team — I speak all of them, and I've built every one in production. Explore the reference blueprints →
A straight line from first call to handoff.
No discovery theater, no proposal decks that outweigh the deliverable. Four steps, and you talk to the same person at every one of them.
Intro call
Thirty minutes on your problem and timeline. I'll tell you plainly whether it's a fit for my skills — and point you elsewhere if it isn't.
Scope & proposal
A written scope with milestones, deliverables, and a fixed price or rate — usually within a week of the first call.
Delivery
I do the work myself, in your repos and your accounts, with weekly checkpoints so there are no surprises at the end.
Handoff
Documentation, runbooks, and working sessions with your team. The goal is that you run it without me.
Four ways to engage
Fixed-price assessment
A scoped review delivered in two to three weeks: findings, gaps, and a prioritized roadmap.
Project engagement
Fixed scope, milestone billing, weeks to a few months. I build it end to end and hand it off.
Architecture retainer
A monthly block of principal-engineer time for reviews, roadmaps, and hard decisions.
Contract embed
Corp-to-corp, direct or through your vendor program. I join your team for a defined run.
The lowest-risk way to start.
Scoped, fixed-fee engagements I deliver in weeks — evaluate the work before committing to a longer build. Larger projects and retainers are quoted after an assessment.
Landing Zone Assessment
A structured review of your AWS organization: account strategy, guardrails, networking, and IaC posture, scored against production-grade reference architecture.
AI Platform Readiness
Can your platform actually run GPU workloads in production? I review capacity, scheduling, cost controls, model-serving architecture, and security end to end.
Zero-Trust Review
An audit of your Zscaler and identity-centric access deployment: policy sprawl, inspection gaps, segmentation, and the findings your next compliance cycle will surface.
Enterprise infrastructure, under enterprise constraints.
Delivery inside audit, change-management, and compliance regimes — financial services, insurance, and healthcare.
Built the global AWS Cloud WAN core network and ran GPU workloads on EKS for a Fortune 500 financial-services enterprise.
Delivered landing-zone and multi-cloud foundations across AWS and Azure — Control Tower, AFT, Service Catalog — that development teams provision against every day.
Stood up container platforms on EKS and ML pipelines on SageMaker and Azure ML in a regulated healthcare-technology environment.
34 certifications. Nine vendors. 18 currently active.
Highlighted credentials are currently active; dimmed ones were previously earned. Full history with credential IDs at vitalemazo.com.
The questions procurement asks first.
How fast can you start?
How do we contract with you?
Do you subcontract or staff a team?
Remote or onsite?
What happens when the engagement ends?
What if the project isn't a fit?
Vitale Mazo, Principal Engineer
I've spent 25 years building and securing enterprise infrastructure — the last decade at the intersection of multi-cloud platforms across AWS, Azure, and GCP, network security, and, most recently, GPU-accelerated AI systems.
I also run a production-grade AI lab — Kubernetes GPU scheduling, self-hosted vLLM model serving, and multi-agent orchestration with persistent memory — where I prove out the architectures before I bring them to client platforms.
Mazo Cloud Group LLC exists for one reason: so organizations can contract that experience directly — one engineer, accountable for the outcome, with no layers in between.
| Legal name | Mazo Cloud Group LLC |
| Structure | Texas limited liability company |
| Location | Austin, Texas — remote-first, US-based |
| Engagement models | Corp-to-corp · fixed-price & project · architecture retainers |
| NAICS | 541512 · 541519 · 541690 |
| Contact | vitale@mazocloudgroup.com · +1 (737) 746-1145 |
Tell me what you're building.
This goes straight to my inbox — no CRM, no tracking, nothing stored beyond the email itself. You'll hear back from me, the person who would do the work, usually within one business day.
Got it. I'll reply within one business day.
Your note is in my inbox. While you wait, here's how I think about the problems most engagements start with:
Engagements contract corp-to-corp with my Texas LLC — W-9, certificate of insurance, and references on request. Happy to work under your MSA or through an existing vendor program.