Jaegeun Han
AI Solutions Architect at AMD
Seoul, South Korea
Summary
👤
Senior🎓
Top SchoolJaegeun Han is an AI Solutions Architect with over a decade of hands-on experience designing and optimizing large-scale GPU-accelerated AI systems for data centers and production LLM pipelines. Currently at AMD, he architects and validates ROCm-based deployments and leads cross-functional programs to boost AI workload performance across GPU, CPU, and DPU platforms. Previously at NAVER he built and stabilized HyperCLOVA X production pipelines, scaled 2,000+ GPU clusters, and delivered long-context and LoRA inference enhancements for FasterTransformer. His background spans deep CUDA optimization, TensorRT tuning, and on-prem GPU cluster deployments from roles at NVIDIA, Samsung, and research labs, with open-source contributions to CUDA educational repos showing practical SGEMM and convolution optimizations. Based in Seoul, he combines systems-level engineering with customer-facing enablement to turn cutting-edge AI research into scalable, production-ready solutions.
10 years of coding experience
13 years of employment as a software developer
Master Degree Candidate Electronic Engineering and Computer Science, Master Degree Candidate Electronic Engineering and Computer Science at Seoul National UniversityBachelor's Degree Media Communication Engineering (Information Technology and Embedded Programming), Bachelor's Degree Media Communication Engineering (Information Technology and Embedded Programming) at Hanyang University
English, Korean
Stackoverflow
27reputation
566reached
0answers
5questions
Github Skills (14)
cuda10
matrix-multiplication10
convolution10
gpu-programming10
parallel-computing10
cprogramming-language9
cudnn9
c-language9
cublas9
vehicle-routing6
sumo6
veins6
attribute6
r6
Programming languages (5)
Github contributions (5)
PacktPublishing/Learn-CUDA-Programming
Feb 2019 - Aug 2020
Learn CUDA Programming, published by Packt
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 44 commits, 5 PRs in 1 year 7 months
Contributions summary:Jaegeun contributed to a CUDA programming repository focused on learning and implementing CUDA applications. The commits demonstrate the user's focus on matrix multiplication (SGEMM) optimization and convolution operations, showcasing their understanding of parallel programming patterns. Further commits refactor and refine various CUDA implementations, including scan operations, and incorporate results validation to ensure correctness. The user also demonstrates the use of CUDA libraries and the integration of profiling tools to measure performance.
cudagpu-accelerationcuda-programminggpupackt
haanjack/haanjack.github.io
Apr 2016 - Mar 2023
Contributions:42 pushes, 3 branches, 10 issues in 6 years 11 months
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