About Ray Swan
I join when operations, commerce, or science have outgrown their systems — and I ship both the product people will use and the AI platform underneath it.
This site is the SYSTEMS catalog: Kitchen Kontrol, NeuroNote, Tokyo Eye, Canon Forge, the Azure MLOps factory, and the AI/ML Engineer Academy. Two resume cuts share that spine — product leader, and GenAI/ML architect. LinkedIn currently wears the architect headline. The work here is the proof, not a studio reel.
Get in Touch
Houston, TX · LinkedIn · GitHub
Professional Summary
GenAI/ML architect and product leader with 25 years delivering enterprise software, data, and transformation programs. Builds governed AI from infrastructure through model and agent runtime — Azure ML, Databricks, Foundry, MLflow, Terraform — and ships the product that actually gets adopted on the floor. I join at the inflection: ops, commerce, or science that has outgrown its systems.
Core Competencies
AI platforms & GenAI
Azure ML, Databricks, Foundry, RAG, agents, MCP tool boundaries
MLOps / LLMOps
Local-to-cloud lifecycle, registry, promotion gates, drift, rollback
Cloud & identity
Terraform, GitHub Actions, Entra/OIDC, least-privilege split
Evidence-bound governance
Immutable metrics, digest-bound apply, unproven work stays off the ledger
Product at the inflection
D2C, subscription, frontline ops — AI inside the workflow, not beside it
Enterprise delivery
Fortune 500 modernization, CRM, commerce, data, integration
Selected Systems
AI/ML Ops Factory
- Azure ML project factory (AIML-SCAFFOLD + Terraform/OIDC) plus a live taxi reference that trained, registered, and batch-served in Dev and Prod.
Tokyo Eye — hyperbolic scientific platform
- Poincaré GNN, e3nn, MoE on FastAPI/pgvector, Terraform Aurora, MLflow gates, XState viewport/discovery/hypothesis machines.
Kitchen Kontrol
- School-nutrition ops: JWT/Express HACCP logs, Ajv forms, Postgres weekly compliance, browser-STT milk logging.
Canon Forge
- Character-reference architect: identity/set/shot contracts in front of Gemini, xAI, Bedrock, and Veo.
Professional Experience
- Turns frontline cafeteria problems into deployable AI-enabled products — ownership, build, integration, and adoption.
- CRM, analytics, commercial-loan origination, API/ETL, and audit-ready acceptance for enterprise workflows.
- D2C, subscription, CRM, e-commerce, and platform programs in Fortune 500 and high-growth environments — including the AI that makes the product adoptable.
Education
M.A., Humanities
California State University, Dominguez Hills
B.A., Economics
University of California, Irvine