Recruiter-readable summary

Machine Intelligence Engineer and Software Architect building multi-agent, AI-memory, and LLM infrastructure.

The specialization leads: agent coordination, persistent AI memory, local LLM/runtime tooling, governed workflows, and enterprise AI integration. More than 20 years of software architecture—and user-confirmed Microsoft Azure delivery in every employment since 2010—provide the production foundation.

Download 2-page resume Experience Structured portfolio evidence

Primary specialization

Machine-intelligence systems.

Agent identity and scope, durable context, governed routing, retrieval, validation, recovery, and human review are treated as system architecture—not prompt-only experimentation.

Open specialization

Multi-agent lane

Coordination for external agents.

Multi-Agent Memory provides public source-available proof for registered identities, scoped access, reviewed memory, messages, acknowledgements, conflict-aware claims, and bounded leases.

Review coordination

AI-memory lane

Persistent context and recovery.

Typed local .uai continuity and reviewed shared-memory patterns preserve identity, constraints, source authority, progress, handoff, and recovery without claiming automatic orchestration.

Review AI memory

LLM-infrastructure lane

Local runtime and developer tooling.

Public .NET packages cover runtime contracts, GGUF/LLaMA components, tensors, tokenization, sampling, managed CPU backends, interoperability, governance, and observability.

Review infrastructure

Azure foundation

Application, data, delivery, and AI services.

Confirmed experience spans App Service, Azure SQL, Blob Storage, Virtual Machines, Azure DevOps, Microsoft Foundry, Azure AI Search, and the earlier Windows Azure/SQL Azure platform.

Review every role

Enterprise foundation

.NET / SQL modernization.

ASP.NET Core, C#, Web API, Razor Pages, EF Core, ADO.NET, SQL Server, stored procedures, indexing, Power BI, DAX, and legacy parity validation.

Open foundation lane

Front-end lane

Angular / RxJS architecture.

Angular is user confirmed at Info724 through an AI documentation review app and at LongTerm Software Solutions through corporate tax accounting software. Current Angular/RxJS patterns support technical depth for reviewers.

Open Angular page

Python lane

AI/data pipeline integration.

Python is positioned for metadata validation, enrichment, extraction, typed API contracts, and reviewed AI/data workflows.

Open Python lane

Capability proof

Career summary evidence matrix.

The summary links conventional career language to proof and reviewer context.

Capability	Evidence	Where to review	Evidence label	Reviewer note
.NET / SQL modernization	Clean resume baseline, experience page, career map, and modernization role lane.	/career/ /experience/ /dotnet-sql-modernization/ Architecture notes	Enterprise Validated	Public summaries protect private source code, client data, scale details, and proprietary schema.
Angular / TypeScript / RxJS	Angular is user-confirmed at Info724 through an AI documentation review app and at LongTerm through corporate tax accounting software; Modern Angular/RxJS material adds current framework fluency for technical reviewers.	/angular-python-architecture/ /case-studies/angular-python-architecture/ Architecture notes Architecture notes /examples/angular22-rxjs-enterprise/README.md	Confirmed Experience	Confirmed Angular delivery is tied to Info724 and LongTerm; Modern Angular/RxJS material communicates current framework fluency.
Python AI/data service boundaries	Python is positioned for metadata validation, enrichment, extraction, and evidence-supported AI/data pipeline work.	/python-ai-data-pipelines/ /ai-prompt-architecture/ Architecture notes /examples/angular22-rxjs-enterprise/backend-reference/README.md	Current Architecture Work	Python examples communicate service-boundary patterns, validation, and AI/data workflow design.
AI memory / handoff / prompt architecture	UAIX, LLMWikis, AI handoff files, portfolio notes, architecture notes, and quality checkpoints.	/ai-memory-handoff/ /ai-prompt-architecture/ Architecture notes Architecture notes	Confirmed Experience	Memory and handoff artifacts show how context, review, and delivery continuity are organized.
Technical mentoring / legacy rescue	Resume-supported mentoring, testing, SOLID, design-pattern, Agile, and modernization work across multiple roles.	/career/ /career-summary/ /experience/ /resume/	Enterprise Validated	Career evidence stays aligned to reviewed resume and experience material.

Human-readable proof path

Start with the resume, then follow evidence.

The resolver remains available, but the career summary gives human readers the direct path first.

Reader	Start	Then review	Professional use
Recruiter	2-page resume	Career Summary, Experience, role lanes	Public baseline only
Hiring manager	Experience	Projects, case studies, domain pages	Private client data protected
Technical reviewer	Case studies	Technical Notes, example source, structured data/API	Review label visible
Structured profile	structured evidence graph	Portfolio API, architecture-note index, and AI memory references	Not a credential issuer

FAQ

Common review questions.

Short answers are rendered in the HTML source and mirrored into JSON-LD where appropriate.

What Azure continuity does the career summary document?

It documents Microsoft Azure use in every professional role since 2010, with service-level details kept beside the employment where each technology was used.

Why is Azure highlighted in the career summary?

The concise summary makes a long-running Microsoft cloud capability visible before reviewers follow the full employment timeline and evidence links.

How does the summary avoid overclaiming?

It uses only user-confirmed services and environment purposes and does not infer scale, outcomes, or private infrastructure.