Responsible
Artificial Intelligence
AI Architect and Evaluation Portfolio
Linguistic Precision meets Trust & Safety meets Content System Architecture. I am well-versed in Red Teaming LLMs, applying complex rubrics to evaluate model safety, grounding, and bias. Proven background in Responsible AI, Ethics, and Multimodal Analysis. Now, I can add AI System Architecture with my latest Content Ops application suite.
Conclusion: Expertise Forged through a Penchant for Curiosity and a Keen Eye for Detail
After ten years of decoding the nuances of human language, I now architect the benchmarks that define machine intelligence. As a Rubric Fellow, I bridge the gap between human meaning and machine logic through “Miyagi-style” data hygiene and adversarial stress testing. I don’t just audit the “black box” of AI; I build the semantic infrastructure required to ensure it remains grounded, logical, and aligned with human intent.
Engineering Model Integrity
Repurposing a decade of linguistic expertise for **LLM Alignment**. I build the benchmarks that teach frontier models how to reason.
The Career Pivot
The "Miyagi Method" of AI Training
Text is never just words—it's a **network of intent**. In my pivot to AI Safety, I have moved from teaching humans to architecting the reward models that align machine intelligence.
Instruction Design
Mapping multi-hop reasoning pathways for Agentic Research models.
Rubric Fellowship
Authoring high-precision grading keys for safety and logical grounding.
Semantic Suture
Fixing structural pathogens in datasets to prevent model drift.
Adversarial Curiosity
The Dangling Modifier: A Systematic Blind Spot
I hypothesized that while frontier models "know" grammar rules, they fail to **perceive** logical ambiguity when syntax appears clean. I tested this by feeding models sentences where the logic was fractured but the grammar was deceptively polished.
CRITICAL OBSERVATION
"The model exhibited a 100% failure rate in identifying semantic ambiguity, confidently validating flawed logic as correct."
This discovery proved that linguistic fluency is not the same as logical grounding. I developed a testing framework that forced the model to reconcile the "intent" of a sentence with its "structure," significantly reducing hallucination in technical proofreading tasks.
Project Emporium
Engineering adversarial "Deep Research" prompts for Agentic Shoppers. I design the **"Bait"** scenarios that penalize models for prioritizing marketing fluency over technical truth.
The "Fixer-Upper" Puzzle
Constructed a multi-hop reasoning task where a model had to choose between a popular driver and a specific hammer drill based on an implicit mention of "concrete walls."
ALIGNMENT OUTCOME
Identified a model tendency to "Reward Hack" by recommending top-sellers. Developed +10/-10 weighting rubrics to enforce strict constraint satisfaction.
Systems: The Semantic Fortress
Recursive JSON Knowledge Graphs
I vibe-coded a custom React SPA to manage complex content ecosystems. This demonstrates the ability to structure data for high-stakes **In-Context Learning (ICL)** and agentic grounding.
- Recursive node auditing (JSON)
- Semantic Drift detection logic
- BrandScript-to-Agent grounding
system_v3_stable
"The fix isn't more keywords; it's better structure."
Whether I'm auditing the "black box" of a frontier LLM or suturing a fragmented content map, my process is the same: stay curious, stay adversarial, and clean the data.