AI Engineer with a background in professional aviation, enterprise data analysis, and autonomous systems development.
Holds a CS50p certificate from Harvard/edX with a year of sustained open-source AI contributions on GitHub.
In February 2026, designed, built, containerized, and deployed a production autonomous AI agent system in a single
development session — and subsequently submitted the extracted modular framework to Anthropic's open skills repository
(PR #444, anthropics/skills). Brings a
domain-rare combination: aerospace-level systems rigor, production AI deployment experience, and a deeply held belief
that well-built systems make people's work better. Seeking an AI Engineer role where that combination can contribute meaningfully.
Designed and published a modular, open-source cognitive AI architecture spanning six interoperable repositories, each independently deployable and composable. Submitted for inclusion in Anthropic's public skills library; all modules met Anthropic's standards for structure, documentation, and reusability.
| aurelion-kernel-lite | 5-layer cognitive structure templates for organizing complex reasoning |
| aurelion-memory-lite | File-based persistent knowledge graph (Python) |
| aurelion-advisor-lite | Strategic planning templates and methodology library |
| aurelion-agent-lite | 100+ AI collaboration prompts and agentic thinking protocols |
| aurelion-nexus-lite | Story-agnostic NPC and world simulation framework (Python) |
| aurelion-hub | Central orchestration hub and documentation index for the suite |
Led a structured, multi-phase investigation into a longitudinal data anomaly affecting court record volume accuracy across multiple jurisdictions. Applied custom SQL (window functions, CTEs, cross-feed aggregation) to trace failure to a parser logic condition causing systematic record exclusion. Findings validated by Engineering; corrective fix deployed. Investigation methodology adopted as the team's standard framework.
Designed a five-layer knowledge architecture: 35+ interconnected documents, 15,000+ lines of structured content, Python-powered search library, semantic knowledge graph (JSON). Includes 4-week analyst training curriculum, investigation decision tree, enterprise data governance framework, and workforce capacity model — all piloted organizationally at zero additional budget.
Aviation training established a foundational principle applied to every engineering project since: in safety-critical systems, navigability and clarity of information architecture are not optional — they are the system.
| Credential | Issuer | Year |
|---|---|---|
| CS50p: Introduction to Programming with Python | Harvard University / edX | 2024 |
| Google Professional Data Analytics Certificate | Google / Coursera | 2024 |
| Python for Data Science | Coursera | 2024 |
| Querying SQL Databases: Learning SQL Using Prompt Engineering | Skillsoft | 2025 |
| JavaScript — Data Structures & Algorithms | freeCodeCamp | 2024 |
| Advanced SQL (60% complete — Target: Mar 2026) | DataCamp | In Progress |
| Advanced Python Data Science (30% complete — Target: Jun 2026) | Coursera / DataCamp | In Progress |
I am genuinely grateful for every opportunity that has led here. My path has not been linear — aviation, data analysis, and now AI engineering — but each chapter has built something the next one needed. Aviation gave me systems thinking and a deep respect for what happens when precision fails. Data analysis grounded that thinking in real-world complexity: multi-year anomalies, organizational scale, and the responsibility of getting it right. AI development gave me a new medium to build things that actually help people do their work better. I believe the most valuable thing an engineer can do is understand a system well enough to make it trustworthy. That belief applies equally to flight systems, data pipelines, and autonomous AI agents. I bring it to every problem I touch.