Building production ML systems that operate at scale. From distributed training on HPC clusters to real-time inference with sub-10ms latency.
97 verified skills across 8 certifications
From Research to Production: Building AI Systems That Scale
I'm an AI Infrastructure Engineer with a rare combination: published research expertise paired with production engineering experience. Currently pursuing my MS in Computer Science at UT Permian Basin while building scalable ML systems that serve millions of predictions daily.
My journey spans from architecting distributed infrastructure for 130+ employees across continents to optimizing ML pipelines that reduced latency by 73% for algorithmic trading. I don't just theorize about systems—I build them, deploy them, and keep them running at 99.9% uptime.
What drives me? The challenge of making AI accessible and reliable at scale. Whether it's implementing distributed training on HPC clusters or building privacy-preserving analytics for libraries, I focus on systems that deliver real value in production.
Production systems and research that push boundaries
Novel multimodal transformer achieving 98.4% accuracy on NSIDC dataset. Fusing optical, SAR, and meteorological data for climate monitoring.
Built ML infrastructure processing 1M+ predictions daily with sub-10ms latency. Reduced compute costs by 40% while improving throughput 3x.
Architected infrastructure supporting 130+ employees across continents. Achieved 99.9% uptime while reducing operational costs by $200K annually.
ML system personalizing course sequences for 32K+ students. Reduced poor recommendations by 66% using contextual bandits.
Privacy-preserving analytics platform for 400K+ volumes. Processing 10M+ API calls with intelligent batch optimization.
Explore my complete portfolio including open-source contributions, research projects, and production systems.
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