I build and validate revenue-critical AI, cloud, network and data platforms—connecting architecture decisions to reliability, capacity, unit economics and controlled production delivery.
A2Z SOC · Architecture review · LinkedIn · Email
My current focus is moving AI and cloud systems from diagrams and pilots into measurable operating platforms: deployable infrastructure, explicit failure modes, business KPIs, evidence boundaries and reproducible decisions.
| System | Painful business problem | Executable proof | Evidence status |
|---|---|---|---|
| Network Change Intelligence Twin | Network changes can interrupt every dependent workload | Intent validation, path analysis, dependency-failure replay, revenue exposure and policy gates | Implemented + simulated; Bicep compiled; no production device operated |
| AI Factory Revenue Twin | GPU capacity, network bottlenecks and cloud placement destroy AI margins | GPU/fabric economics, hybrid IaaS comparison, CloudStack/Kubernetes contracts and 30 KPIs | Implemented + Azure evidence plane deployed; hardware telemetry simulated |
| Enterprise AI Integration Platform | Revenue stalls between CRM, ERP, payments, logistics and billing | Durable workflow replay, idempotency, compensation, exposure API and 25 KPIs | Implemented + simulated; SAP/Salesforce/Oracle adapters are contracts |
| CompoundCloud AI Delivery Fabric | Teams scale the wrong AI model, cloud topology or capacity profile | Multicloud architecture compiler, transaction replay, failure recovery and unit economics | Implemented + Azure evidence plane deployed; provider pricing is reference data |
| AI-Native Internal Developer Platform | Platform teams cannot deliver environments consistently or economically | Kubernetes, GitOps, infrastructure contracts, golden paths and delivery economics | Implemented reference platform; integrations retain explicit boundaries |
| Real-Time Payment Fraud Platform | Fraud controls block legitimate revenue or miss coordinated attacks | Streaming risk API, graph signals, false-decline economics and Azure architecture | Implemented + simulated; synthetic transactions, not a bank deployment |
- AI infrastructure: inference capacity, GPU utilization, model routing, latency, network fabrics and cost per successful outcome.
- Cloud and platform engineering: Azure, multicloud, Kubernetes, GitOps, internal developer platforms, migration and disaster recovery.
- Network architecture and automation: BGP, hybrid connectivity, network digital twins, pre-change validation, Ansible and infrastructure as code.
- Enterprise and real-time data: Kafka, APIs, durable workflows, SAP/Salesforce/Oracle boundaries, Microsoft Fabric, analytics and transaction reliability.
- Reliability and operations: OpenTelemetry, SRE, failure injection, RTO/RPO, rollback, KPI gates and evidence receipts.
- Security and compliance: identity, segmentation, policy as code, agent authorization, SOC engineering and evidence derived from deployed state.
BUILD & SCALE CONNECT & OPERATE
AI factories Enterprise integration
Cloud and Kubernetes Real-time data and APIs
Developer platforms AIOps and observability
Migration and modernization SAP / Salesforce / Oracle
OPTIMIZE BUSINESS VALUE VERIFY & RECOVER
Inference and GPU economics Network change assurance
Payment fraud economics Disaster recovery
Supply-chain digital twins Security and compliance
Capacity and FinOps Controlled agent execution
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 or adapter is designed but has not called the real provider.
Modeled revenue, savings, latency and capacity are not presented as customer outcomes. SHA-256 receipts provide tamper-evidence for the serialized decision; they do not provide non-repudiation without an authenticated signing and custody system.
- Real-Time AI Data Platform — streaming, Microsoft Fabric, Power BI, lakehouse and predictive/prescriptive analytics.
- AIOps Observability Platform — OpenTelemetry, Kubernetes monitoring, root-cause analysis and incident automation.
- Cloud Resilience & Disaster Recovery — RTO/RPO, ransomware recovery, multi-region planning and chaos scenarios.
- Supply Chain Digital Twin — forecasting, inventory optimization, disruption response and working-capital economics.
- Execution and replay: aegis-runtime, agent-wal, agent-dag-lock.
- Identity and authorization: agent-jit-iam, agent-kill-switch.
- Tool and MCP security: agent-schema-firewall, mcp-shield, zero-leak-dlp.
- Evaluation and cost: agent-eval-guard, agent-finops, agent-cost-cascade.
- Retrieval integrity: graph-rag-guard, vector-index-sanitizer.
These are focused components and experiments, not claims that a single monolithic production platform has deployed every subsystem.
I am best suited to customer-facing architecture and forward-deployed work where the objective is measurable:
- architecture and unit-economics assessments;
- AI/cloud/network platform design and implementation;
- production-readiness and reliability programs;
- enterprise integration and data-platform delivery;
- managed infrastructure, observability and optimization;
- security and compliance engineering derived from the operating architecture.
Have a revenue-critical AI, cloud or network platform that needs to scale reliably?