AI Solutions Architect building secure, self-hosted agentic AI and RAG systems grounded in trusted data and attributable sources.
I combine hands-on AI engineering with cybersecurity, data governance, and enterprise architecture experience. I build systems that connect language models with trusted data, APIs, and enterprise tools while treating security, privacy, auditability, and operational risk as architecture requirements rather than afterthoughts.
Self-hosted web research platform combining Firecrawl, SearXNG, Ollama and Playwright with unified REST and MCP APIs
Evidence-grounded agentic AI that searches and scrapes authoritative sources, uses an LLM for classification, then deterministically verifies its cited evidence before accepting the result.
A self-hosted RAG pipeline with semantic search, cross-encoder reranking, and source attribution — built to show how the pieces fit together and what it takes to secure them.
Built to recover dependency visibility from an enterprise Tableau environment where hundreds of unmanaged, unversioned workbooks had accumulated on network shares, allowing database teams to assess the downstream impact of schema changes before breaking dashboards.
I designed, built, and operate a self-hosted agentic AI platform used for real-world travel research and planning. It combines:
- LangGraph orchestration
- Model Context Protocol (MCP) tools
- Retrieval-augmented generation (RAG)
- Qdrant vector search
- FastAPI services
- Ollama local inference
- Web search and scraping
- Source validation and citation tracking
- Docker-based deployment
Reusable agentic workflows have reduced research tasks that previously took 2–3 hours to under 10 minutes.
Security is part of the architecture, not a separate compliance step.
As a CISSP, my work has included:
- Vulnerability management and threat-intelligence integration
- Secure, self-hosted AI architecture for sensitive data
- Permissioned access to tools and data sources
- Data governance, lineage, privacy, and auditability
- Enterprise security, retention, reliability, and operational risk
At The Walt Disney Company, I built an AWS-hosted historical vulnerability pipeline and automated correlation between internal vulnerability data and external threat intelligence to improve remediation prioritization.
AI & Agents
LangGraph · MCP · FastMCP · RAG · Ollama · Qdrant · Embeddings
Security & Governance
CISSP · Vulnerability Management · Threat Intelligence · Data Governance ·
Privacy · Auditability · Operational Risk
Development
Python · FastAPI · Flask · Streamlit · REST APIs · JupyterLab
Data & Analytics
PostgreSQL · MongoDB · Neo4j · MySQL · SQLAlchemy · Tableau · Alteryx · Dataiku
Infrastructure & DevOps
Docker · Linux · AWS · GitHub Actions · CI/CD · Cloudflare Tunnels
My background spans AI architecture, cybersecurity, enterprise data systems, analytics, product development, and technical program leadership.
Earlier in my career, I held product, program, and alliance leadership roles at Microsoft and Siemens.
I then spent nearly a decade in safety-critical commercial diving and hyperbaric operations, where reliability, procedure, training, compliance, and operational risk were everyday responsibilities.
More recently, my consulting work has included:
- Charles Schwab — Python-based Tableau lineage and dependency analysis across hundreds of workbooks
- Evernorth Health Services — rebuilt Neo4j ingestion pipelines, improving reliability from approximately 65% to 99.9%
Today, I apply that combination of enterprise technology, security, governance, and operational-risk experience to building practical AI systems.
- B.A.Sc., Computer Engineering — University of Waterloo
- CISSP — Certified Information Systems Security Professional
- AWS Certified AI Practitioner
- CDMP — Certified Data Management Professional
- CAIP — Certified Artificial Intelligence Practitioner