Jack O'Brien

I am a computer science graduate with a minor in cybersecurity who focuses on improving AI systems through rigorous data validation, annotation, and tooling. I blend practical engineering with analytical rigor to raise model reliability and dataset quality.

In industry roles I evaluated AI outputs, corrected generated code, and validated security labels across large datasets, directly reducing model errors and improving downstream performance. I also design test cases, prompt strategies, and documentation to close gaps and scale best practices.

My projects include building a FastAPI-based local LLM router with logging and model-selection capabilities, and an AI-driven nutritional deficiency detector using Flask and GPT-4. I build dashboards and automation to make insights actionable for stakeholders.

I bring hands-on experience with Python, SQL, JavaScript, FastAPI, Flask, Docker, and Tableau, and I seek roles where I can apply annotation workflows, data validation, and model-evaluation processes to improve product outcomes and reliability.