I design and validate revenue-critical AI and cloud platforms across Azure, AWS, GCP, Kubernetes and hybrid networks.

My work connects infrastructure architecture, network reliability, GPU and cloud economics, observability, Infrastructure as Code, SOC engineering and continuously verified controls—turning operational findings into reviewable changes and measurable outcomes.

View flagship system · Explore A2Z SOC · Request an architecture review · LinkedIn

Available for remote Principal Architect, Forward-Deployed Engineer, AI Infrastructure, Cloud Platform and customer-facing CISO/GRC engineering engagements internationally.

The control loop consumes normalized operational evidence from three independently testable cloud adapters:

• Azure Compliance Automation — Azure Policy and Checkov/Terraform evidence.
• AWS Compliance Automation — Security Hub, AWS Config and Checkov/Terraform evidence.
• GCP Compliance Automation — Security Command Center, Cloud Asset Inventory and Checkov/Terraform evidence.

Each adapter produces normalized control observations, SHA-256 integrity digests and review-gated CISO Assistant synchronization plans. CISO Assistant remains the GRC system of record; the adapters and control loop provide the technical collection, architecture decision and verification layers.

• AI infrastructure and Agentic AI: inference capacity, GPU utilization, model routing, context and workflow reliability, controlled tool execution and cost per successful outcome.
• Cloud and platform engineering: Azure, AWS, GCP, Kubernetes, GitOps, internal developer platforms, migration, modernization and disaster recovery.
• Network architecture and automation: BGP, hybrid connectivity, private networking, DNS, network digital twins, pre-change validation, Ansible and Infrastructure as Code.
• Reliability, data and FinOps: OpenTelemetry, SRE, streaming systems, failure injection, RTO/RPO, rollback, capacity planning and unit economics.
• CISO, SOC and GRC engineering: security architecture, detection and monitoring, control design, ISO 27001/42001 readiness, evidence automation, policy-as-code, risk treatment and remediation verification.

I treat governance as an engineering feedback loop derived from deployed systems—not a spreadsheet layer separated from operations:

Cloud, network, identity, application and SOC telemetry
                         ↓
           Normalized technical evidence
                         ↓
     Controls, risks, findings and audit workflows
                         ↓
   Terraform / OpenTofu / Bicep / Ansible proposal
                         ↓
         Human approval and controlled rollout
                         ↓
       Recollection and remediation verification

Relevant capabilities include:

• CISO Assistant integration and multi-cloud evidence collection;
• ISO 27001, ISO 42001, SOC 2, NIST, CIS, NIS2 and DORA mapping workflows;
• Microsoft Sentinel, Wazuh, OpenSearch and cloud-native SOC architectures;
• identity, segmentation, logging, detection engineering and incident evidence;
• audit readiness, evidence lifecycle, third-party risk and corrective-action tracking;
• agent authorization, MCP security and human-governed remediation.

Framework mappings and modeled outcomes are never presented as certification, legal advice or customer results without the corresponding review and evidence.

Every flagship separates four evidence classes:

• Implemented — executable code and automated tests exist.
• Deployed — retained evidence comes from an authorized cloud or infrastructure environment.
• Simulated — deterministic fixtures or synthetic telemetry exercise declared scenarios.
• Contract — an integration boundary is designed but has not called the real provider.

Modeled revenue, savings, latency, capacity and risk reduction are not presented as customer outcomes. SHA-256 receipts demonstrate integrity of serialized decisions; they do not provide non-repudiation without authenticated signing and evidence custody.

• Agentic DevOps & SRE Skill Registry — evaluated reusable skills for CloudOps, SRE, Kubernetes, networking and FinOps.
• AI Factory Revenue Twin — GPU, fabric, capacity and hybrid-cloud unit economics.
• Enterprise AI Integration Platform — durable orchestration across CRM, ERP, payments, logistics and billing boundaries.
• AI-Native Internal Developer Platform — Kubernetes, GitOps, golden paths and platform delivery economics.
• AIOps Observability Platform — OpenTelemetry, root-cause analysis and incident automation.
• Cloud Resilience & Disaster Recovery — RTO/RPO, ransomware recovery and multi-region scenarios.

Earlier post-quantum, healthcare, biometric, robotics and domain-specific systems remain available in my repositories, but do not represent my primary commercial positioning.

Cloud, AI, network and data topology; failure modes; capacity; security boundaries; operating KPIs and unit economics.

Operational findings through Infrastructure as Code, cost and blast-radius review, controlled rollout and verified evidence.

Cloud and network operations, observability, FinOps, SOC integration, compliance-as-code, audit evidence and continuous improvement.

Have a revenue-critical AI, cloud or network platform that must scale reliably and remain governable?

Request an architecture and unit-economics review.