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AI Systems Architect shaping both production LLM systems and how engineering orgs build with AI | Remote
I take LLM systems from research to reliable production, and I shape how engineering organizations build with AI while doing it. The work across Meta, Amazon, Stability AI, Postscript, and TeamSense has a consistent shape: the gap between "the model works in a notebook" and "the system works for users at scale." At Postscript and TeamSense, that expanded into organizational scope. The AI-assisted development workflows, evaluation standards, and code review norms I introduced became the default across both engineering orgs, shaping how every engineer ships AI features, not just the ones I work with directly. My strongest contributions tend to operate at two levels simultaneously: product systems (multi-agent orchestration, evaluation frameworks that catch behavioral drift before it ships) and the engineering practices that determine whether an organization can actually build on AI sustainably. A deliberate career pattern of entering AI subfields before they consolidate, which compounds. Early-stage technical ambiguity and optimizing internal organizational patterns around AI is where I do my best work.
Georgia Institute of Technology
Master's degree, Computer Science
January 1, 2019 – January 1, 2022
California State University Channel Islands
Bachelor’s Degree, Computer Science
N/A – Present
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Cultural Fit Analysis
The candidate has worked across various company sizes, from startups to large enterprises (Amazon, Meta, Stability AI), and in diverse domains (HR tech, e-commerce, AR, gaming, content moderation). This breadth of experience suggests adaptability and a strong cultural fit for dynamic environments. Their focus on establishing best practices and driving organizational change indicates a proactive and influential approach, which can be highly beneficial for a senior role. The consistent progression into leadership and specialized AI roles demonstrates a clear career trajectory and commitment to the field.
Soft Skills & Operational Fit
The candidate's resume highlights leadership roles (Lead Generative AI Engineer, Lead AI Engineer, Lead Software Engineer) and a focus on establishing engineering standards and workflows, indicating strong leadership, communication, and operational skills. Their experience in cross-functional collaboration (product, data science, infrastructure) suggests good team collaboration and strategic thinking. The descriptions also show a proactive approach to problem-solving and process improvement.