Bachelor's Degree: Mechanical Engineering

Hometown: Boise, Idaho

I don't just manage projects, I architect predictable systems. My focus is on replacing tactical drag and firefighting with declarative frameworks that enforce security, scalability, and profitability as a structural reflex, regardless of the underlying tech stack.

I specialize in bridging strategy and engineering: connecting big-picture goals with detailed technical execution. My career began with hands-on engineering roles at NVIDIA and Google X and grew to leading mission-critical infrastructure migrations within complex, revenue-driving environments.

Core Competencies

• Agentic AI & Privacy Architecture: Inventor of a patented methodology for State-Aware Anonymity in multi-modal interactions (US12482164B2), solving the paradox of personalized utility vs. data privacy.

• Infrastructure Optimization: Expert in directing high-stakes migrations and Zero-to-One pipelines. I specialize in authoring RFCs and establishing architectures that ensure 100% data parity and eliminate configuration drift.

• Forensic Governance: I utilize structured methodologies, from DFM and Stage-Gate to Agile and Decision Registers, to maintain project velocity while protecting teams from burnout and system entropy.

The Why: Outside of my professional role, I became obsessed with the privacy paradox of agentic AI. For an AI agent to be truly useful, it needs deep context, but that context often contains PII (Personally Identifiable Information). I wanted to solve the core architectural problem: How can an agent convey a user's intent without exposing their identity?

The Complexity: The difficulty lies in dynamic context filtering. Standard data masking is static, it simply redacts names or numbers. However, in a multi-modal agentic interaction (voice, video, or text), identity is often embedded in the state of the conversation. I had to teach myself how to architect a system that could differentiate between contextual intent (what the user wants) and identity markers (who the user is) in real-time.

The Outcome: I deep-dived into the intersection of privacy and tokenization, researching how to build privacy rails at the ingestion layer. I developed a methodology for state-aware anonymity that dynamically scales the level of data abstraction based on the agent's current task. I synthesized this logic into a functional methodology that was successfully granted as US Patent 12482164B2.