Summary
Taylor Bird is a Senior Principal AI/ML Data Architect with 11 years of experience building production-scale systems for federal and private clients, now leading mission-focused data solutions at Slalom in the DC market. He has a track record at Excella of architecting high-throughput ML pipelines, identity resolution platforms, and autoscaling MLOps that process millions of records daily and cut operational latency through intelligent automation. A practical GenAI practitioner, Taylor has designed RAG frameworks and foundation-model integrations tailored for federal use cases while driving firm-wide AI strategy, hands-on training, and cross-functional mentorship. His background spans full-stack and enterprise Java development through to deep reinforcement learning study, giving him rare fluency across legacy modernization and cutting-edge ML deployment. Colleagues rely on him to translate complex regulatory and operational constraints into scalable, auditable AI systems that deliver measurable efficiencies.
11 years of coding experience
17 years of employment as a software developer
Oakton High School
Nanodegree Program Deep Reinforcement Learning, Nanodegree Program Deep Reinforcement Learning at Udacity
George Mason University
BS Computer Science, BS Computer Science at William & Mary