Résumé
Senior Software Consultant & Project Technical Owner — Tulsa, OK
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Full-stack software consultant with 15+ years of professional experience delivering enterprise web applications across diverse industries. Combines hands-on architecture and development in C#/.NET, Angular, Blazor, SQL Server, and Azure with project leadership responsibilities including client engagement, estimation, team management, QA oversight, and mentorship. Proven ability to embed with client teams, adopt their conventions, and ship production systems on time. Passionate about R&D, developer onboarding, and continuous improvement. Client names omitted here under NDA — available on request during a conversation.
Custom business systems built on .NET / C#, Blazor, React, Angular, or Vue — with clean architecture your team can maintain after I'm gone.
I've moved MVC → Angular, WebForms → ASP.NET Core + Angular, and AngularJS → modern Angular. Same backend where possible, far less risk.
Certified Azure Developer Associate. Repos into Azure DevOps, CI/CD pipelines, Bicep infrastructure, Blob Storage, and failover that survives the bad day.
Local-first AI platforms: pgvector hybrid retrieval, NL-to-SQL, knowledge graphs, and local model serving on consumer hardware — no per-token bill shock.
The part most devs skip. I've owned client engagement end-to-end — scoping, estimates, PM, QA oversight, delivery — across MVP through v3 releases.
4+ bootcamp cohorts trained in ABP Zero/IO, Angular, C#, and SQL. If you need a team that stays capable after the project, this is how you get one.
C# · TypeScript · JavaScript · Python · SQL · PowerShell
.NET / ASP.NET Core · Blazor Server + MudBlazor · ABP Framework (Zero/IO) · EF Core · Web API · SignalR · Background jobs
Angular 2–17 · React · Vue 2 · Bootstrap · PrimeNG · Metronic theming
SQL Server (DACPAC) · PostgreSQL + pgvector · Azure DevOps Pipelines · Bicep / IaaC · Blob Storage · Docker · CI/CD
RAG pipelines · Local LLM serving (llama.cpp/GGUF) · Embeddings & hybrid retrieval · NL-to-SQL · Knowledge graphs · MCP tooling
Supabase (Postgres + Auth + Storage) · Vercel deployment & edge functions · Bolt.new / Lovable-style app builders · Next.js App Router · Tailwind CSS v4 · Cursor / Copilot-assisted workflows · Resend transactional email
bUnit / Playwright E2E · Performance tuning · Client engagement & estimation · Project management · QA oversight · Team mentorship
Architecture and hands-on delivery of a template-driven demand & purchase-order workflow platform with policy-based RBAC.
Full-stack ownership on an enterprise SaaS platform with tenant-scoped access and event-driven updates.
Python / PostgreSQL / pgvector hybrid-retrieval system: vector search + keyword search + query rewriting + NL-to-SQL + knowledge-graph traversal.
Windows-first serving platform running multiple GGUF models (1.5B–4B) on a single RTX 3050 4 GB via llama.cpp + CUDA.
Solo ownership across MVP → v3: client engagement, PM, QA, PTO, development. Sub-30-minute start-to-finish lending application delivered T&M.
Migrated a 7-year-old high-traffic portal while reusing the backend to accelerate delivery, then refactored to modern best practices.
Full employment history — Momentum3 (senior consultant, 2015–present), Seed Technologies, CLS Group, Megasys HMS, Ok-2-Way LLC — available on request.
Since large language models started integrating directly into code editors, the shape of software consulting has changed in a way that benefits clients more than it threatens them. My daily loop is now human architecture + machine throughput: I design systems, own the judgment calls, and use LLMs to compress the hours between decisions.
A lot of “AI development” is marketing for cloud token bills. I run local-first AI infrastructure instead — llama.cpp serving GGUF models on consumer GPUs behind an OpenAI-compatible endpoint, wired straight into VS Code. Client data never leaves my machine during development, iteration is near-instant, and there's no per-token meter running while we build.
The same stack powers client-facing products: RAG chatbots over your own documents with hybrid retrieval (vectors + keyword + query rewriting), NL-to-SQL analytics, knowledge-graph traversal, and MCP endpoints so existing tools can talk to your data. When AI belongs in a product, I build it like plumbing — boring, testable, observable — not like a demo.
$ session --mode local
▸ architect: human (me)
▸ throughput: LLM-assisted
▸ models: GGUF @ localhost:8080
▸ data_egress: none during dev
▸ rag: pgvector + BM25 + rewrite
▸ nl_to_sql: yes
▸ mcp_endpoints: chat/analytics/health
# vram: 4GB. it works.
ITT Technical Institute
Associate of Science, Computer Sciences · 2011
Earned June 2022 · Expires June 2027
