Target role guides

Recruiter-readable paths from resume to proof.

These role guides translate portfolio evidence into clear hiring paths led by Machine Intelligence, multi-agent systems, persistent AI memory, LLM infrastructure, and enterprise AI architecture. Azure/.NET/SQL modernization, TypeScript/Angular, and bounded Python AI/data work remain the production foundation.

Download 2-page resume Open structured evidence View case studies
Machine Intelligence Engineer / Software Architect Machine intelligence engineer and software architect specializing in multi-agent systems, persistent AI memory, LLM infrastructure, local model tooling, governed AI workflows, and enterprise AI integration, backed by more than two decades of .NET, Azure, SQL Server, and production software engineering. Multi-Agent Systems Architect Design and implementation evidence for governed coordination among external AI agents without claiming agent hosting, execution, distributed consensus, or production scale. AI Memory and Agent Coordination Engineer Persistent, source-bounded agent continuity and reviewed durable memory with explicit governance and human verification. LLM Infrastructure / Local Model Tooling Engineer A 17-package .NET 9 local GGUF/LLaMA runtime family with a verified managed-CPU execution path. Microsoft Azure / .NET / SQL Modernization Architect User-confirmed Microsoft Azure delivery across every employment since 2010, paired with .NET and SQL modernization, parity validation, and testable architecture. Angular / TypeScript / RxJS Reviewer Path For reviewers evaluating user-confirmed Angular work, TypeScript, RxJS, component boundaries, CI/CD delivery context, and enterprise front-end patterns while preserving the Angular/RxJS implementation sample boundary. Python AI / Data Pipeline Boundary For teams needing Python-side validation, enrichment, metadata extraction, AI-assisted documentation, and review-gated data output behind typed contracts. AI Memory / Handoff Systems Architect For teams evaluating source-governed AI workflows, file handoff, project handoff, compact .uai memory, long-term docs, and agent-readable portfolio data.

Role path

Machine Intelligence Engineer / Software Architect

Machine intelligence engineer and software architect specializing in multi-agent systems, persistent AI memory, LLM infrastructure, local model tooling, governed AI workflows, and enterprise AI integration, backed by more than two decades of .NET, Azure, SQL Server, and production software engineering.

Primary evidence

  • Multi-Agent Memory public source-available coordination and persistent-memory reference implementation.
  • 17-package .NET 9 local GGUF/LLaMA runtime family with a verified managed-CPU execution path.
  • 32 public NuGet packages with 21K+ cumulative downloads.
  • More than two decades of .NET, Azure, SQL Server, TypeScript, and production software engineering.

Technical signals

Multi-agent systems Persistent AI memory LLM infrastructure Local model tooling Governed AI workflows Enterprise AI integration Software architecture Microsoft Azure .NET and C# SQL Server TypeScript

Suggested review path

  • /machine-intelligence/
  • /multi-agent-systems/
  • /ai-memory/
  • /llm-infrastructure/
  • https://github.com/MichaelKappel/Multi-Agent-Memory
  • https://multiagentmemory.com/

Resume lines

  • Machine intelligence engineer and software architect specializing in multi-agent systems, persistent AI memory, LLM infrastructure, local model tooling, governed AI workflows, and enterprise AI integration, backed by more than two decades of .NET, Azure, SQL Server, and production software engineering.
What reviewers can see Multi-Agent Memory public source-available coordination and persistent-memory reference implementation.
Reviewer note This lane organizes public evidence; it does not imply autonomous production scale, customer adoption, or private infrastructure not shown by the cited sources.

Role path

Multi-Agent Systems Architect

Design and implementation evidence for governed coordination among external AI agents without claiming agent hosting, execution, distributed consensus, or production scale.

Primary evidence

  • A private-intranet reference implementation for coordinating external AI agents through registered identities, governed scopes, durable reviewed memory, coordination rooms, current-message delivery, acknowledgements, and conflict-aware hash-and-lease coordination for local .uai edits.
  • Registered identities, immutable scope grants, coordination rooms, current-message delivery, per-recipient acknowledgements, and conflict-aware hash-and-lease coordination are documented publicly.

Technical signals

multi-agent systems agent coordination registered identities governed scopes coordination rooms acknowledgements .uai hash-and-lease coordination

Suggested review path

  • /multi-agent-systems/
  • https://github.com/MichaelKappel/Multi-Agent-Memory
  • https://multiagentmemory.com/

Resume lines

  • Design and implementation evidence for governed coordination among external AI agents without claiming agent hosting, execution, distributed consensus, or production scale.
What reviewers can see A private-intranet reference implementation for coordinating external AI agents through registered identities, governed scopes, durable reviewed memory, coordination rooms, current-message delivery, acknowledgements, and conflict-aware hash-and-lease coordination for local .uai edits.
Reviewer note Source-available reference implementation; not OSI-approved open source, not a hosted SaaS product, and not proof of production scale. It coordinates external agents but does not host or execute them.

Role path

AI Memory and Agent Coordination Engineer

Persistent, source-bounded agent continuity and reviewed durable memory with explicit governance and human verification.

Primary evidence

  • Typed .uai identity, instructions, constraints, context, progress, handoff, and recovery records.
  • Multi-Agent Memory durable scopes, reviewed public-safe memory, targeted and broadcast messages, and acknowledgements.

Technical signals

persistent AI memory .uai durable scopes reviewed memory agent handoff source governance human verification

Suggested review path

  • /ai-memory/
  • https://github.com/MichaelKappel/Multi-Agent-Memory
  • https://multiagentmemory.com/
  • Architecture notes

Resume lines

  • Persistent, source-bounded agent continuity and reviewed durable memory with explicit governance and human verification.
What reviewers can see Typed .uai identity, instructions, constraints, context, progress, handoff, and recovery records.
Reviewer note AI handoff provides typed local first-load continuity; Multi-Agent Memory adds governed shared coordination and reviewed durable memory. Neither grants credentials, certifies truth, or authorizes execution.

Role path

LLM Infrastructure / Local Model Tooling Engineer

A 17-package .NET 9 local GGUF/LLaMA runtime family with a verified managed-CPU execution path.

Primary evidence

  • A 17-package .NET 9 local GGUF/LLaMA runtime family with a verified managed-CPU execution path.
  • 32 public NuGet packages with 21K+ cumulative downloads.

Technical signals

LLM infrastructure local model tooling .NET 9 GGUF LLaMA tokenization sampling quantized CPU kernels deterministic sessions backend diagnostics

Suggested review path

  • /llm-infrastructure/
  • /projects/#runtime-family
  • https://www.nuget.org/profiles/Michael.Kappel

Resume lines

  • A 17-package .NET 9 local GGUF/LLaMA runtime family with a verified managed-CPU execution path.
What reviewers can see A 17-package .NET 9 local GGUF/LLaMA runtime family with a verified managed-CPU execution path.
Reviewer note GPU packages expose registration, capability, and fail-closed diagnostics; they do not establish GPU inference.

Role path

Microsoft Azure / .NET / SQL Modernization Architect

User-confirmed Microsoft Azure delivery across every employment since 2010, paired with .NET and SQL modernization, parity validation, and testable architecture.

Primary evidence

  • User-confirmed role-level Azure evidence across all nine employments from 2010 to Present
  • Info724 / Insurance 724 modernization
  • LongTerm Software Solutions architecture rescue
  • SQL Server stored-procedure scenario validation
  • Transportation/logistics service patterns

Technical signals

Microsoft Azure Azure App Service Azure SQL Azure Blob Storage Azure Virtual Machines Azure DevOps Microsoft Foundry Azure AI Foundry Azure AI Search Windows Azure SQL Azure ASP.NET Core C# Razor Pages Web API EF Core ADO.NET SQL Server Stored Procedures Parity validation Automated comparison screens Legacy behavior preservation

Suggested review path

  • /experience/
  • /resume/
  • /career/
  • Architecture notes
  • /case-studies/
  • /projects/
  • Architecture notes

Resume lines

  • Microsoft Azure used in every employment since 2010, with role-specific services documented in the public resume and experience timeline.
  • Rebuilt dental insurance contract management functionality using ASP.NET Core, C#, TypeScript, EF Core, and Razor Pages.
  • Created automated comparison screens and test coverage to validate rebuilt claims-adjudication behavior.
What reviewers can see Can map legacy behavior into maintainable .NET and SQL-backed systems with validation seams.
Reviewer note Keeps separate from a risk-free rewrite; it claims evidence of parity-focused modernization patterns.

Role path

Angular / TypeScript / RxJS Reviewer Path

For reviewers evaluating user-confirmed Angular work, TypeScript, RxJS, component boundaries, CI/CD delivery context, and enterprise front-end patterns while preserving the Angular/RxJS implementation sample boundary.

Primary evidence

  • Info724 Angular/TypeScript AI documentation review app for an Info724 client
  • LongTerm corporate tax accounting Angular work
  • LongTerm CI/CD pipeline usage in the tax-system consulting engagement
  • Angular / RxJS implementation example
  • Angular/Python architecture page and .uai memory mapping

Technical signals

Angular TypeScript RxJS AI documentation UI corporate tax accounting software CI/CD pipelines Angular/RxJS implementation sample SSR SSG Hydration Typed DTOs Data-access store Vitest Playwright SEO-first rendering

Suggested review path

  • /angular-python-architecture/
  • /case-studies/angular-python-architecture/
  • Architecture notes
  • Architecture notes
  • /examples/angular22-rxjs-enterprise/

Resume lines

  • Info724: Angular/TypeScript AI documentation review app for an Info724 client, enabling users to step through AI-generated documentation for existing codebases.
  • LongTerm Software Solutions: Angular used extensively on corporate tax accounting software, with CI/CD pipeline work in that consulting engagement.
What reviewers can see Shows user-confirmed Angular experience at Info724 and LongTerm plus a current Modern Angular enterprise architecture example.
Reviewer note Keeps separate from that MikeKappel.com is an Angular app or that a production project currently runs Modern Angular.

Role path

Python AI / Data Pipeline Boundary

For teams needing Python-side validation, enrichment, metadata extraction, AI-assisted documentation, and review-gated data output behind typed contracts.

Primary evidence

  • Python-oriented AI utility pipelines
  • NeuralWikis / LocalEndpoint reviewed in context project lanes
  • OpenAPI contract examples
  • Human review boundary in AI architecture docs

Technical signals

Python FastAPI reference pattern Pydantic models MySQL Metadata validation AI/data enrichment OpenAPI Typed JSON Human review gate

Suggested review path

  • /ai-prompt-architecture/
  • /case-studies/neuralwikis-localendpoint-ai-boundaries/
  • Architecture notes
  • Architecture notes

Resume lines

  • Architected AI-assisted engineering workflows using OpenAI and LM Studio APIs to support legacy-to-modern code translation, semantic search, documentation, and developer productivity.
What reviewers can see Can define safe service boundaries where generated or enriched data remains reviewable before publication.
Reviewer note Keeps separate from external AI APIs or private customer data are called from the public example code.

Role path

AI Memory / Handoff Systems Architect

For teams evaluating source-governed AI workflows, file handoff, project handoff, compact .uai memory, long-term docs, and agent-readable portfolio data.

Primary evidence

  • UAIX AI Memory Package Wizard
  • LLMWikis trust-labeled knowledge systems
  • /docs long-memory inventory
  • .uai short-term receiver files
  • Static JSON and REST profile endpoints

Technical signals

AI Memory UAIX UAI-1 .uai File Handoff Project Handoff Source governance Agent-readable docs JSON/API profile Human review

Suggested review path

  • /ai-prompt-architecture/
  • Architecture notes
  • Architecture notes
  • /future-profile/

Resume lines

  • AI-assisted engineering, semantic search, automated documentation, and agent workflows are supported by the clean resume baseline.
What reviewers can see Shows a durable handoff architecture for AI-assisted work that separates active memory from reviewed long-term docs.
Reviewer note Keeps separate from automatic execution authority, independent truth certification, or replacement of human review.

Capability portfolio evidence map

Same proof, multiple reviewer paths.

The graph keeps role targeting, project evidence, Architecture Notes, structured data, API references, and reviewer paths connected.

Resume Role path Project / case study architecture notes AI knowledge references structured data / API Review

FAQ

Common review questions.

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

What are the technical role guides?

They are recruiter-readable paths connecting the resume to bounded evidence for machine intelligence, multi-agent systems, AI memory, LLM infrastructure, enterprise AI integration, and the Azure/.NET/SQL foundation.

Do role guides create new experience claims?

No. They reorganize existing reviewed or evidence-labeled material so recruiters and technical reviewers can reach the most relevant proof faster.