Dzung Tran
Principal Engineer Technical Advisor ScalApps
Portland, Oregon Metropolitan Area United States
Summary
👤
Senior🎓
Top SchoolDzung Tran is a seasoned engineering leader and founder with over 30 years building AI platforms, cloud infrastructure, and distributed systems that run in production at global scale. At Intel he led 40+ engineers across AI Security, ML Engineering, DevOps and cloud platform teams, delivering toolkits, explainability and governance workflows, and secure Kubernetes architectures that supported 25+ ML initiatives across a 500+ engineer organization. He combines hands-on MLOps expertise—contributing practical improvements to Intel’s AI reference models and performance tooling—with strategic delivery of DevSecOps transformations and large-scale telemetry and analytics platforms. Now leading ScalApps as a technical advisor, he applies enterprise lessons to focused advisory and hands-on exploration while evaluating senior leadership roles. Named inventor on 12 U.S. patents, he pairs deep technical curiosity with a track record of shipping secure, reliable AI systems.
12 years of coding experience
4 years of employment as a software developer
Bachelors of Science Electrical Engineering, Bachelors of Science Electrical Engineering at University of PortlandMasters of Science Computer Science (emphasis on HCI), Masters of Science Computer Science (emphasis on HCI) at Portland State University
Github Skills (21)
docker10
performance-monitor10
performance-analytics10
python10
dockers10
performance-measurement10
performance-analysis10
tensorflow10
performance-tuning10
performance-monitoring10
cicd9
machine-learning9
inference9
cpu9
ai9
Programming languages (7)
Github contributions (5)
intel/ai-reference-models
Oct 2020 - Nov 2021
Intel® AI Reference Models: contains Intel optimizations for running deep learning workloads on Intel® Xeon® Scalable processors and Intel® Data Center GPUs
Role in this project:
MLOps Engineer
MLOps EngineerContributions:25 commits, 1 comment in 1 year 1 month
Contributions summary:Dzung primarily contributed to improving the usability of the Intel AI Reference Models by enhancing the performance analysis capabilities. The user implemented a Dockerfile for a sample notebook, enabling easy setup and execution of performance benchmarks. Further commits addressed system configurations, particularly regarding NUMA nodes and core utilization, optimizing the environment for different hardware setups. The user also refactored the TFX pipeline for compatibility and streamlined the installation and uninstallation processes.
optimizationsprocessorstensorflowzoomodel-zoo
dzungductran/blaster
Sep 2015 - Dec 2015
Contributions:74 commits, 59 pushes, 1 branch in 3 months
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