Intelligent Autonomous Systems & Innovation Architect | AI Systems Engineer | Inventor of Original Technologies

Engineering Portfolio · Original Technologies · Strategic Opportunities · LinkedIn

Engineer by nature, systems architect by design.

I invent, architect and engineer original intelligent technologies for problems without ready-made specifications. My work spans AI Agent Brain engineering, stateful autonomous decision systems, governed execution, multidisciplinary software architecture and physical engineering. I take undefined objectives through system design, implementation, integration, testing and evidence-based validation.

I use multiple AI systems as specialized engineering resources across research, architecture, implementation, code review, debugging and testing. I retain responsibility for the design decisions, constraints, integrated system behavior and release standards.

Independently architected and integrated an agentic AI platform using specialized Agent Brains with scoped knowledge, persistent operational memory, contextual state, knowledge provenance, objectives and approval-governed external actions.

Engineering scope includes multi-model orchestration, MCP/API-connected capabilities, structured tool contracts, semantic and policy validation, dry-run execution, observability, fault recovery and human-in-the-loop governance.

Evidence: 38/38 focused offline checks passed after an evidence-binding defect was corrected. Broader regression remained incomplete at that milestone, so the candidate was not released publicly. This is recorded focused test evidence, not a claim of production deployment.

Public architecture and engineering evidence

Independently engineered stateful decision infrastructure connecting real-time inputs, operating state, probabilistic evaluation, deterministic risk and authority gates, external interfaces, lifecycle reconciliation, persistent telemetry, replay and forward-only observation.

A frozen prospective observer isolates validation from authority to change policy or execute external actions. The architecture emphasizes timing integrity, evidence preservation and controllable failure states.

Evidence: 14/14 isolated observer-control tests passed. These checks establish control behavior only, not verified strategy effectiveness, financial returns or live-capital deployment.

Public architecture and engineering evidence

Both projects are described at a public-safe architectural level. Their private implementation repositories are not published here.

Vehicle Intelligence Reference Architecture Lab™

A working synthetic software demonstrator exploring vehicle engineering intelligence: developing-fault precursors, probabilistic hypothesis revision, bounded preservation, condition-based maintenance, ranked engineering recommendations, design requirements and comparable-condition validation.

Evidence: 18/18 focused synthetic reference tests passed. The public reference does not represent real-vehicle control, OEM integration or independently validated production performance.

Run the interactive demonstrator · Inspect public Python repository · Technical overview

A public engineering reference for the critical boundary between AI reasoning and permission to act. It demonstrates explicit tool authority, human approval gates, controlled execution, pre-execution and post-execution validation, auditable evidence and bounded failure recovery.

Evidence: 4/4 focused synthetic reference tests passed. This is an architectural demonstration, not a claim of enterprise production deployment.

Inspect public repository · Technical overview

Conceived and developed the VB500 SuperGT, based on the 2022 Subaru WRX, integrating powertrain, thermal management, braking, suspension and chassis-performance systems. This is physical vehicle development, distinct from the synthetic V.I.R.A.L. demonstration.

RZ1 Performance Engineering · VB500 SuperGT

Engineering foundations include mission-critical enterprise computing environments serving major investment banks, electrical and electronic engineering, complex infrastructure diagnostics, mechanical systems, commercial technology operations and multidisciplinary systems integration. The emphasis is on constraints, failure diagnosis and end-to-end engineering accountability across digital and physical domains.

Define the constraint. Build the complete decision pathway. Separate intelligence from authority. Verify the outcome. Preserve the evidence.

My work connects initial problem framing to executable architectures, controlled implementation, system-level review, reproducible validation and explicit release decisions. Test outcomes are reported within their actual scopes. Design concepts are not represented as deployed systems.

I consider funded original technology development, advanced engineering commissions, strategic retainers, technology partnerships and exceptional remote senior engineering mandates. Commercial discussions can concern new technology invention, defined development milestones or the appropriate rights to a specific existing technology. Background intellectual property and engagement terms are handled explicitly.

Explore Strategic Opportunities · Engineering Portfolio · Three-page V9 Résumé · LinkedIn

Validation counts above describe specific recorded checks. They do not imply independent certification, continuing CI status, commercial traction or production deployment.