Taehyun Kim
Head Of Startup SAs, Korea at Amazon Web Services (AWS)
Seoul, South Korea
Summary
👤
Senior🎓
Top SchoolTaehyun Kim is a seasoned engineering leader and AWS specialist with over 14 years of software engineering experience and a decade-plus focus on data engineering, real-time ETL, and personalized recommendation systems. As Head of Startup SAs Korea at AWS, he blends hands-on expertise in Hadoop, Spark, Kafka, HBase, Storm, Solr and ML tooling with strategic leadership of teams developing scalable analytics and ML solutions. He previously led personalization, recommendation, and ranking teams at Coupang and built search and crawler systems at NAVER and SK planet, bringing product-minded engineering to large-scale data problems. Proficient in Python and Java, Taehyun has deep operational experience deploying ML workloads on AWS—illustrated by contributions to a Korean-language AWS AI/ML workshop and a SageMaker-based movie recommendation notebook. He’s known for turning complex streaming and batch pipelines into production-grade systems and for emphasizing cost- and resource-aware ML operations. Based in Seoul, he pairs cloud-native architecture fluency with a pragmatic focus on measurable business impact.
9 years of coding experience
15 years of employment as a software developer
Master’s Degree, Computer Science, Master’s Degree, Computer Science at Yonsei University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (15)
factoring10
amazon-sagemaker10
2factor10
jupyter-notebook10
factors10
recommendation-system10
machine-learning10
python10
factorization10
ml10
aws9
algorithms8
data-analysis7
deeplearning-ai7
deep-learning7
Programming languages (1)
Github contributions (5)
aws-samples/aws-ai-ml-workshop-kr
Sep 2018 - Nov 2018
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 2 pushes, 1 branch in 1 month
Contributions summary:Taehyun contributed to a machine learning project focused on movie recommendation using Amazon SageMaker and Factorization Machines. Their work included adding a notebook detailing the implementation of a movie recommendation system, including data loading, preprocessing, model training, deployment, and prediction. The user also updated the notebook by adding a step to delete the endpoint, ensuring resource management.
sagemakerhands-onmxnetai-mlworkshop-materials
hyun0131/spark-the-definitive-guide-codes
Dec 2018 - Nov 2019
Contributions:8 pushes, 1 branch in 11 months
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