Portfolio guide

How to review this portfolio from resume summary to technical evidence.

Start with the Machine Intelligence specialization, then move into implementation proof and the human-facing career path. Multi-agent systems, persistent AI memory, LLM infrastructure, governed AI workflows, and enterprise AI integration lead; Microsoft Azure, .NET, SQL, TypeScript, Angular, and Python remain the production engineering foundation.

Download 2-page resume Career path Experience Case studies

How to review Michael

Choose the right depth before opening the archive.

This is the canonical reviewer map: short scan, recruiter path, hiring-manager path, technical proof, AI-readable data, and enterprise foundation.

30 seconds

Positioning scan

Confirm the headline role, strongest proof answer, and exact claim boundary before reading the broader archive.

Machine Intelligence Proof brief

2 minutes

Recruiter path

Use the concise resume, current contact path, and role-level experience timeline to qualify fit quickly.

Resume Experience Contact

10 minutes

Hiring-manager path

Move from role fit into selected projects and case studies, then inspect the Evidence Map if a claim needs traceability.

Review guide Projects Case Studies Evidence Map

Deep dive

Technical architecture path

Inspect Multi-Agent Memory, persistent AI memory, local LLM infrastructure, package evidence, validation, and non-claims.

Multi-Agent Memory case Multi-agent brief LLM infrastructure NuGet packages

AI-readable

Machine-readable review path

Use this path for AI agents, answer engines, ATS tools, and structured review without treating hidden files as public claims.

llms.txt llms-full.txt Route index AI agent manifest

Proof first

Machine-intelligence claim path

Answer the core proof question directly, then branch into multi-agent systems, AI memory, LLM infrastructure, and Intelligence724 boundaries.

Proof answer Multi-Agent Systems AI Memory Intelligence724 boundary

Foundation

Azure / .NET / SQL path

Confirm the enterprise engineering foundation with role-level Azure usage, .NET/SQL modernization, and conservative evidence boundaries.

Experience timeline Azure evidence brief .NET / SQL modernization

Claim to proof matrix

What each major claim proves - and what it does not prove.

This matrix keeps SEO, AEO, GEO, recruiter review, and technical review aligned to the same conservative evidence boundaries.

Machine Intelligence Engineer / Software Architect

Proof: Machine Intelligence overview, proof brief, resume, case studies, and Evidence Map.

Boundary: Does not claim foundation-model training, novel ML research, benchmark leadership, or autonomous systems without human governance.

/machine-intelligence/Architecture notes /resume/

Multi-agent systems and coordination architecture

Proof: Multi-Agent Systems page, Multi-Agent Memory case study, public repository, companion docs, and evidence brief.

Boundary: Does not claim hosted SaaS, OSI-approved open source, production scale, distributed consensus, model hosting, or automatic conflict-free merging.

/multi-agent-systems//case-studies/multi-agent-memory/Architecture notes

Persistent AI memory and handoff

Proof: AI Memory page, AI memory handoff route, persistent memory evidence brief, and .uai handoff records.

Boundary: Does not claim automatic sync, automatic memory promotion, private repository writes, credential access, or runtime orchestration authority.

/ai-memory//ai-memory-handoff/Architecture notes

LLM infrastructure and local runtime tooling

Proof: LLM Infrastructure page, NuGet package profile, package map, and LLM infrastructure evidence brief.

Boundary: Does not claim foundation-model training, public GPU inference benchmarks, or unverified production inference scale.

/llm-infrastructure//nuget-packages/Architecture notes

Azure / .NET / SQL enterprise architecture

Proof: Experience timeline, resume, .NET/SQL modernization route, and Azure evidence brief.

Boundary: Does not infer unconfirmed scale, private infrastructure, certifications, client names, or business outcomes.

/experience//resume/Architecture notes

Code intelligence and AI-assisted modernization

Proof: Experience page, Intelligence724 public capability surface, and code-intelligence boundary brief.

Boundary: Does not disclose confidential Info724/client systems or claim that Intelligence724 and Info724 are the same legal entity.

/experience/Architecture notes

Research reports as supporting context

Proof: Reports archive, research search, docs manifest, and curated Architecture Notes promoted after review.

Boundary: Raw reports are not resume claims; sensitive/search-only records stay out of navigation and sitemaps unless searched for.

Research archive /research-search/Architecture notes

Runtime parity

Confirm the deployed package before treating live pages as current.

The ZIP package, static JSON files, and deployed WordPress runtime can drift if the package has not been installed yet. Use these checks before citing live behavior.

Version proof Open /route-qa-contract.json, /research-dashboard.json, or the runtime health endpoint and confirm the current package version.

Human routes After version proof, review Career, Experience, Case Studies, Evidence Map, Resume, and Contact in the installed WordPress runtime.

REST routes Validate WordPress REST JSON separately from static files so deployed endpoint behavior is not inferred from package-local validation.

Boundary If production reports an older version, document deployment drift and do not claim current-package live QA.

Route QA JSON Runtime health JSON Research dashboard JSON

Recommended order

Review in five passes.

This keeps the portfolio easy to scan while preserving deeper technical evidence for architecture review.

01 Read the resume first

Use the two-page resume as the concise career and stack baseline.

Resume page

02 Check career and experience

Confirm chronology, roles, mentoring, Microsoft-stack depth, Angular experience, and CI/CD context.

Experience

03 Open target role paths

Use role lanes for Machine Intelligence, multi-agent systems, persistent AI memory, LLM infrastructure, enterprise AI architecture, and the supporting Azure/.NET/SQL foundation.

Role paths

04 Inspect case studies

Use case studies when a hiring manager or architect needs technical substance behind the resume.

Case studies

05 Use evidence and structured data last

Architecture Notes, structured data, and the Research Library are deeper reviewer tools after the resume and experience path.

Public route index

Stack priority

Primary stack first, supporting lanes clearly visible.

The site states the center of gravity plainly before deeper project metadata.

Primary specialization Machine Intelligence / Multi-Agent Systems / AI Memory / LLM Infrastructure

Enterprise foundation Microsoft Azure / .NET / SQL / TypeScript / Angular / Python

Angular evidence Info724 AI documentation review app and LongTerm corporate tax accounting software.

Python lane AI/data pipeline boundaries, metadata validation, extraction, and service-layer reference patterns.

Architecture proof Projects, case studies, Architecture Notes, and structured portfolio data.

Evidence labels

Plain-English labels used across the site.

Internal verification values remain available in the technical appendix, but human-facing pages use these labels.

Public label	Meaning for reviewers	Where to verify
Enterprise Validated	Stable professional or curated portfolio evidence.	Resume, experience, projects, case studies.
Experience Confirmed	Directly confirmed by Mike or supported by the clean resume baseline.	Career, experience, Angular page, resume page.
Current Architecture Work	Reference work that shows current architecture thinking and future-facing technical exploration.	Architecture Notes, Portfolio Notes, Research Library.
Research Library	Background research retained for search, comparison, and deeper review.	Research Library and Research Library Search.

Reviewer shortcut

Use the right page for the right question.

Recruiter or hiring manager Resume, Career, Experience, and Target Roles.

Senior technical reviewer Case Studies, Portfolio Evidence, and Architecture Notes.

Angular reviewer Experience, Angular/RxJS Architecture, and Angular case study.

AI architecture reviewer AI Architecture, AI Knowledge Architecture, and reviewed handoff notes.

Research reviewer Research Library Search and Portfolio Notes with clear labels.

Confidentiality and professional presentation

Business-safe proof with private details protected.

The site demonstrates capability through public summaries, selected project surfaces, architecture notes, and structured portfolio data while keeping private source code, client data, credentials, production connection details, proprietary schema, and scale-sensitive details out of the public view.

Structured appendix for ATS, AI-assisted review, and technical screening

These references preserve exact structured data while the page above uses human-facing labels.

Evidence graph data Resume data Research Library Search Portfolio Notes Structured guide data

FAQ

Common review questions.

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

Where should a recruiter start?

Start with the two-page resume, then use the career and experience pages for context. Projects, case studies, architecture notes, and structured data are available for deeper review.

What is the main technology stack?

The public hierarchy leads with Machine Intelligence, multi-agent systems, persistent AI memory, and LLM infrastructure. Microsoft Azure, .NET, SQL, TypeScript/Angular, and bounded Python work provide the production engineering foundation.

How should the Research Library be used?

Research archive material is background context and can inform curated architecture notes after review.

REST contract

Validate WordPress REST endpoints only after deployed-version proof.

Use these links only after the runtime reports the current package version. Package-local OpenAPI parity does not prove deployed endpoint behavior.

Reviewer note

Recommended review order.

Start with the resume, career, and experience pages; move into role paths, case studies, Architecture Notes, and research only when deeper technical proof is needed.

Enterprise Validated