About
From Emergency Management to AI Engineering
I design and lead the development of AI-enabled systems for complex, high-stakes operational environments.
My career has been defined by building and scaling programs that matter — national chemical preparedness initiatives, pandemic response coordination, multi-agency crisis operations. These experiences taught me how to think in systems, manage complexity, and deliver results when failure has real consequences.
Now I’m applying that same mindset to a different challenge: designing production-grade AI platforms and cloud architectures that can be deployed, operated, and trusted in real-world environments.
Two Decades of High-Stakes Program Leadership
I bring 20+ years as an action-oriented project and program management professional in emergency management, with deep expertise in chemical and biological threats.
Core Experience
- National Program Development — Standing up and scaling federal chemical preparedness and response capabilities
- Interagency Coordination — Driving complex initiatives from concept through implementation across DOD, HHS, DHS, and state/local agencies
- Crisis Action Planning — Leading operational planning for pandemics, hurricanes, wildfires, and CBRN incidents
- Multidisciplinary Expertise — Spanning biotechnology, CBRN/HAZMAT operations, medical countermeasures policy, and federal portfolio management
This isn’t theoretical work. It’s experience gained in Operations Centers, interagency working groups, and real incidents where decisions had to be right the first time.
Why AI Systems and Cloud Architecture
I’m focused on AI systems and cloud architecture because they enable scalable decision support, automation, and data-driven capabilities in exactly the kinds of complex operational environments I’ve spent my career in.
But my interest goes beyond building models. I’m focused on designing the full platforms that support them:
- Data pipelines that feed AI systems
- Service layers that make them accessible
- Monitoring and observability that make them trustworthy
- Deployment architectures that make them operational
Building Technical Capability with an Architectural Mindset
Rather than positioning myself as a pure individual contributor engineer, I’m intentionally building toward roles that combine technical depth with system architecture, platform design, and technical leadership.
My goal: understand how to design AI-enabled platforms end-to-end — from service boundaries and data flows to deployment models, observability, and lifecycle management.
Technical Capabilities in Development
🤖 AI & LLM Systems
- End-to-end LLM application architectures - Retrieval-Augmented Generation (RAG) system design - Multi-step LLM orchestration and workflow design - Evaluation strategies and monitoring of model behavior - Responsible integration of LLMs into operational systems⚙️ Backend Platforms & APIs
- Service-oriented backend architecture - API design for AI-driven platforms - Async workflows and background processing models - Logging, monitoring, and system observability💾 Data Platforms
- Data modeling for AI-enabled applications - MongoDB for document and workflow persistence - SQL for structured data and analytics - Schema and versioning strategies for evolving systems☁️ Cloud & Infrastructure Architecture
- Cloud-native system design on AWS - Compute, storage, and networking architecture - Environment separation (dev/staging/prod) - Security, scalability, and cost considerations - Deployment and operational architecture patterns🛠️ Languages & Tools
- Python (primary language) - SQL (PostgreSQL, MySQL) - Git and collaborative workflows - Testing frameworks (pytest, unittest) - CI/CD pipelinesHow Emergency Management Shapes My Technical Approach
My work in emergency management and CBRN operations requires disciplined systems thinking, risk awareness, and operational planning. I bring that same mindset into technical architecture:
| Emergency Management Principle | Technical Architecture Application |
|---|---|
| Plan for failure modes | Design for graceful degradation and recovery |
| Make risks explicit | Document architectural tradeoffs clearly |
| Prioritize reliability | Choose simplicity over unnecessary complexity |
| Enable operations | Ensure systems can be monitored and evolved |
| Maintain accountability | Build in observability and auditability |
This perspective shapes how I approach AI and cloud architecture, particularly in environments where safety, accountability, and resilience matter.
What You’ll Find on This Site
This portfolio documents my transition through system designs, hands-on projects, and architectural writeups.
Content Types
📂 Project Case Studies
End-to-end system designs with architectural deep-dives, tradeoff analyses, and implementation details
🏗️ Architecture Documentation
Platform design patterns, service boundaries, data flows, and deployment models
📝 Engineering Notes
Technical learning, problem-solving approaches, and lessons from building real systems
🎯 Leadership Philosophy
How decades of high-stakes leadership inform my approach to technical work
Values & Leadership
Beyond technical work, I bring a leadership philosophy shaped by decades in high-stakes environments. My approach centers on:
- Dignity — Treating people with respect, especially under pressure
- Character — Leading by example and maintaining integrity
- Empowerment — Building capability in others, not dependency
- Vulnerability — Admitting uncertainty and creating space for others to do the same
- Curiosity — Approaching problems with genuine interest in understanding
Technical excellence matters, but so does how you lead, how you treat people, and how you build teams.
Read Full Leadership Philosophy →
Connect
If you’re interested in:
- AI system architecture and production AI
- Cloud platform design for operational systems
- Applying AI in complex, high-stakes domains
- Bridging operational experience with technical capability
I welcome the opportunity to connect.