Travis crew (Ghost)

Data Analytics student (Western Governors University, Junior standing) and AI Engineer with 1+ year of hands-on experience building and shipping production machine learning systems, including two published open-source Python packages for quantum computing and hyperdimensional computing. Skilled in Python, machine learning (ML), large language model (LLM) fine-tuning, AI agent infrastructure, and data pipeline development. One year of experience in data annotation and model evaluation supporting reinforcement learning from human feedback (RLHF) for frontier AI models.

Seeking an AI Engineer role in Houston, TX.

AI Engineer at Strategic Innovations AI (2025-05 – Present)

• Built and published two open-source Python packages (Catalyst-Q, Catalyst-Brain) to PyPI, together supporting distributed quantum computing simulation and hyperdimensional computing for AI agent infrastructure.
• Fine-tuned large language models (LLMs) for legal information-retrieval and reasoning tasks, improving domain-specific response accuracy through iterative evaluation cycles.
• Developed a peptide discovery pipeline integrating quantum optimization algorithms with machine learning models for early-stage drug-candidate screening.
• Benchmarked quantum circuit simulations using metriq-gym, identifying and resolving performance bottlenecks across gate-level implementations.
• Authored technical documentation and a research proposal for the DARPA Quantum Benchmarking Initiative (QBI), translating complex technical work for non-technical review.
• Applied to Y Combinator with a product and technical roadmap for the company's AI/quantum computing platform.

AI Data Annotation Specialist at Remote (Contract) (2025-05 – 2026-05)

• Evaluated and labeled AI model outputs across coding, reasoning, and multi-domain tasks to support reinforcement learning from human feedback (RLHF) pipelines for large language models.
• Wrote and reviewed reference solutions and code samples, assessing correctness, efficiency, and reasoning quality against defined rubrics.
• Maintained consistent accuracy and throughput across high-volume annotation projects, contributing directly to measurable improvements in model output quality.

B.S. in Data Analytics – Western Governors University (2027)