Website · Credentials · Projects
I am an engineering and architecture leader with 18+ years of experience turning ambiguous enterprise problems into working systems. I combine executive discovery and enterprise architecture with hands-on delivery of agentic AI, RAG, cloud-native platforms, and repeatable adoption playbooks.
My experience spans public sector, SaaS, transportation, financial services, automotive, and technology.
| Experience | Team leadership | Automation | Operations |
|---|---|---|---|
| 18+ years | 12-person team | 95%+ | 25% lower |
| Engineering and architecture | Agentic RAG delivery | RCA report generation | Mean time to resolution |
Additional outcomes include 40% less manual support intervention and 30% fewer customer escalations and reopened requests through AI-assisted triage and knowledge retrieval.
| Project | What it demonstrates |
|---|---|
| construct.da Live demonstration |
Evidence-backed development-approval screening for Australian residential projects. Document ingestion, OCR, spatial data, asynchronous workflows, and advisory reporting in a full-stack TypeScript application. |
| Mandate | Agentic AI assurance and policy generation with guided onboarding, LangGraph orchestration, web research, streaming progress, and deterministic EU AI Act risk classification. |
| Intelligent Briefing Notes | Architecture and implementation reference for durable, multi-party approval workflows using Temporal, including state transitions, retries, observability, testing, and operational scaling. |
| AI Security | Practical knowledge base covering AI governance, privacy, adversarial ML, model risk, security controls, and assurance frameworks. |
- Forward-deployed AI: customer discovery, workflow mapping, rapid prototyping, technical validation, and production handover
- Reliable agentic systems: RAG, tool use, multi-agent orchestration, evaluation, guardrails, observability, and human review
- AI governance and assurance: converting policies, risk frameworks, and regulatory requirements into practical engineering controls
- Cloud and platform architecture: OCI, AWS, Azure, Kubernetes, serverless, APIs, integrations, and event-driven systems
- Engineering leadership: creating clarity, raising technical standards, and enabling teams to deliver through ambiguity
Python TypeScript JavaScript Java Go SQL React Next.js Spring Boot LangGraph LangChain Temporal PostgreSQL Kubernetes OCI AWS Azure
- Start with the user workflow and a measurable outcome.
- Make assumptions, constraints, and failure modes explicit.
- Build the smallest working system that can prove or disprove value.
- Instrument quality, reliability, cost, and adoption.
- Turn repeated delivery patterns into capabilities other teams can reuse.
Enterprise AI · Forward-Deployed Engineering · Architecture · Delivery Leadership