I work at the intersection of AI evaluation, quality assurance, applied AI systems, technical problem solving, and workflow design.
My background combines Computer Science, long-term technical support and QA-focused analysis, and current independent work involving AI evaluation and training, multimodal assessment, workflow development, technical enablement, and applied AI projects.
- AI Evaluation & QA — response evaluation, preference ranking, rubric-based assessment, task design, edge-case analysis, and multimodal review
- Applied AI Systems — turning ideas and ambiguous requirements into structured, testable workflows and decision logic
- QA / UAT & Debugging — testing behavior, identifying failure patterns, troubleshooting issues, and validating revisions
- Workflow Design & Optimization — designing reusable processes, human-review checkpoints, and practical technical workflows
- Technical Enablement — translating complex system behavior into understandable guidance and usable solutions
- Hands-on Prototyping — working with Python, APIs, structured data, scripting, and AI-assisted development
A Thinkorswim/ThinkScript decision-support project developed through iterative requirements analysis, QA/UAT, debugging, and live-chart validation.
The system evolved from a simple volume-warning concept into a configurable workflow combining trend, exhaustion, volume context, graded NORMAL / CAUTION / HALT states, reason labels, persistence controls, and user-configurable behavior.
Focus: Requirements Analysis · QA/UAT · Debugging · Systems Thinking · Decision-Support Design
An evolving customer-support AI project focused on conversational memory, support knowledge, evaluation logic, failure handling, and structured interaction workflows.
The project is also being used for hands-on work with Python, APIs, JSON, prompt design, and AI-system evaluation.
Focus: Conversational AI · Python · APIs · Workflow Design · Evaluation · Technical Support
A modular application concept for extracting and organizing evidence from video and other source material, including transcripts, screenshots, metadata, structured outputs, and provenance.
The project emphasizes modular architecture, evidence quality, human review, failure handling, traceability, and repeatable workflows.
Focus: Applied AI Systems · Python · Workflow Architecture · Evidence & Provenance · QA · Human-in-the-Loop Design
AI Evaluation Quality Assurance UAT Requirements Analysis
Workflow Design Workflow Optimization Debugging Technical Enablement
Python APIs JSON ThinkScript Technical Documentation
Prompt & Rubric Design Multimodal Evaluation Systems Thinking
This profile documents independent project work, technical experimentation, and applied AI/QA workflows. Individual repositories provide additional project-specific evidence and documentation as they are published.