Woosik Seol is a data engineer and scientist with nine years of experience building production ML/AI systems and big data platforms across GCP, AWS, and Hadoop ecosystems. He has moved models to scale using Airflow/Oozie orchestration, Spark/Hive processing, and modern DWs like Iceberg and BigQuery, while also owning MLOps workflows to tackle latency and model drift. His background spans recommendation and fraud systems at consumer giants to blockchain asset-tracking prototypes and enterprise-grade RAG/LLM fine-tuning, reflecting a rare blend of applied NLP/LLM work (LangChain, HuggingFace, vector DBs) and classical feature-driven modeling. Comfortable in polyglot stacks (Python, Java, Scala) and cloud-native deployments (Docker, Kubernetes, Vertex AI, SageMaker), he pairs hands-on engineering with consulting and pre/post-sales roles. Based in Seoul, he values enjoyable development and often integrates practical data privacy considerations into pipeline design.
Contributions:124 commits, 458 pushes, 2 branches in 3 months
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