Résumé: Jeayoung Jeon
MLOps and Cloud-Native Engineer
My name is Jeayoung Jeon [🇰🇷전제영], and I’m a software engineer in South Korea.
Currently, I’m working at MAXST as an MLOps, DevOps, and Cloud-Native Software Engineer. I also specialize in:
- 🧑🔬 Contributed to the launch of a Digital Twin Platform project by developing Cloud-Native APIs and ML Pipelines.
- 🐳 Developing and operating a Hybrid Kubernetes Cluster for High Availability and GPU Cost Reduction.
- 🧑🔧 Supporting Internal Services and Deployment Cycles using MLOps/DevOps.
- 🧑🎓 Leveraging background in Computer Vision, ADAS, and ML to contribute DevOps and decision aligned with business objectives.
- jyjeon@outlook.com
- LinkedIn: jyje
- Google Scholar
- Google Scholar: Jeayoung Jeon
- GitHub
- Github
- StackShare
- StackShare
Projects
– (7 Months)
🧑💻 Widearth: Digital Twin Platform with Spatial Map & AR Contents at MAXST
Roles: Development of ML pipelines, APIs and Infrastructure
- DevOps Designed CI/CD pipelines for web servers and ML workloads. Set git-flow environments with GitOps and led decision-making for deployments.
- Hybrid Cluster Built hybrid clusters with AWS EKS and bare-metal Kubernetes. The ML pipelines are executed on on-premise clusters to optimize GPU costs. Backup pipelines are configured on EKS to increase availability.
- ML Pipeline & API Designed Argo Workflows based ML data pipelines to generate spatial maps. Developed cloud-native API endpoints managing lifecycle of pipelines.
Results: 'Contribute Dev & Ops' <- Built Hybrid Clusters, ML Pipelines, and CI/CD Pipeline [contrib 75%+]
- Main DevOps Manage CI/CD for Widearth project. Leading 40+ deployments for 3 months.
- Robust Hybrid Infra Achieved '96% availability/year and 14d downtime' using hybrid clusters and DevOps support.
- ML Pipeline Designed ML APIs and data pipelines in multi-clusters. Reduces costs of public cloud by 50%.
Skills: Core Skills for Project Widearth
- AWS EKS
- Karpenter
- Python FastAPI
- Argo Workflows
- Argo CD
– (6 Months)
🧑🔬 MLOps: On-premise MLOps with the Latest Open Source Projects at MAXST
Roles: Built Core MLOps Platform using CNCF Open Source Projects
- AutoML Making AutoML tuning hyperparameters with Katib and Argo Workflows without pre-build.
- Distributed Training Developing distributed learning environments using Kubeflow Training Operator.
- JupyterHub Generating On-Demand JupyterNotebook to distribute resources for ML researchers.
Results: 'Initiate MLOps' <- Improve GPU utilization for AI research using Kubeflow, JubeterHub [contrib 90%+]
- MLOps Applied latest open sources to improve the on-premises research environment.
- GPU Utilization Increased GPU utilization by 3 times and conducted more than 800 AutoML experiments.
Skills: Core Skills for On-Premise MLOps
- Kubeflow/Katib
- Kubeflow/Training Operator
- Argo Workflows
- Grafana
- TensorBoard
– (12 Months)
🧑🔧 DevOps: Development of Hybrid Clusters Providing CI/CD and Chatbot at MAXST
Roles: Development of Hybrid Clusters, CI/CD Pipelines, and Chatbot
- Hybrid Cluster Built a hybrid cluster with AWS EKS and on-premise Kubernetes. GPU workloads are executed on on-premise clusters to optimize costs. Web and backup workloads are configured on EKS to increase availability.
- IaC IaC with Terraform and Ansible to manage the cluster infrastructure: Terraform to set up AWS EKS cluster. Ansible-based Kubespray to set up on-premises cluster.
- CI/CD Configured fast CI for collaboration using Bitbucket Pipeline. Configured high-performance custom CI using on-premises Argo Workflows. Implemented CD using GitOps with Argo CD and Slackbot. IaC was also configured as CI/CD and pipeline to set up declarative infrastructure.
Results: 'Initiate DevOps' <- Developed Hybrid Clusters using AWS EKS and On-Premise [contrib 75%+]
- Robust Hybrid Cluster Achieved 50%+ cost reduction compared to pure cloud infrastructure using on-premises cost-effectiveness.
- DevOps Culture Propagation of DevOps culture including app modernization and CI/CD. Decision support through monitoring.
Skills: Core Skills for Hybrid DevOps
- Kubernetes
- Argo Workflows
- AWS EKS
- IaC
- Terraform
- Python/FastAPI
- Python/Bolt (Slack)
– (2 years)
📸 Digital Twin Research Engineer at MAXST
Roles: Development of computer vision algorithms and construction of digital twin systems
- Visual-SLAM & SfM Developed digital image processing algorithms for Visual-SLAM and SfM. Constructed a digital twin system using image processing algorithms.
- Technical Research Personnel Engaged in computer vision positions related to graduate school majors and performed military alternative service.
Results: 'Proof of Concepts' <- Algorithm research for digital twin systems [contrib 50%]
- Digital Twins Research and development of Visual-SLAM and ICP algorithms for digital twin systems
- Automation Development of automated pipelines for data acquisition and analysis
Skills: Core skills for digital twin research
- Computer Vision
- SfM
- Visual-SLAM
- Python
- OpenCV
- .NET/C#
- Unity
– (8 Years)
🧑🎓 Digital Signal Processing and ADAS Researcher (Integrated Program) at POSTECH
Roles: Studying and researching in the field of digital signal processing and computer vision
- 2018 - 2020 Computing and Control Engineering Lab. (Prof. SH, Han)
- 2012 - 2018 Advanced Signal Processing Lab. (Prod. H, Jeong)
- Real-Time Advanced Driver Assistance Systems using FPGA
- Research on Traffic Sign & Lane Terrain Detection
- Research on Stereo Vision & Markov Random Fields
Results: 'R&D' <- Studying on Automotive Simulations in Virtual Environments and ADAS On-Edge.
- Digital Twins Virtual Visual-SLAM for Real-World Environments
- Edge ADAS Research of ADAS including Traffic Sign Detection & Lane Terrain Detection with FPGA
Skills: Core Skills for ADAS Research
- Computer Vision
- Digital Signal Processing
- Markov Random Fields
- ADAS
- Traffic Sign Detection
- Lane Terrain Detection
- MATLAB/Simulink
- C/C++
Work
– present
💼 Senior Software Engineer [🇰🇷 책임연구원] at MAXST
Roles: Developed On-Premise Clusters Providing MLOps for Technology Division in MAXST
- MLOps Developing on-premise clusters providing MLOps for the AI team.
- DevOps Building hybrid clusters with AWS EKS and bare-metal Kubernetes.
- Hybrid Building on-premise clusters with IaC tools such as Ansible and Kubespray.
Skills
- Kubernetes
- On-Premise
- AWS
- Argo Workflows
- Data Pipeline
- CI/CD
- Computer Vision
- OpenCV
– (3 Years)
💼 Software Engineer [🇰🇷 선임연구원] at MAXST
Roles: Associate R&D Engineer for Technology Division in MAXST
- Algorithm Research Reviewing computer vision algorithms in state-of-art papers and implementing prototypes.
- DevOps Building hybrid clusters and providing data pipelines for digital twins.
- Technical Research Personnel Serving as a substitute for military service for 3 years, engaging in the industry in the related field of computer vision major.
Skills
- Kubernetes
- On-Premise
- AWS
- Argo Workflows
- Data Pipeline
- CI/CD
- Computer Vision
- OpenCV
Education
–
🎓 Master's Degree (Integrated Program) in Department of Electrical Engineering, Signal Processing & Computer Vision from Pohang University of Science and Technology (POSTECH) with GPA of 3.2/4.3
–
🎓 Bachelor's Degree in School of Electronic Engineering, Electronic Communication from Kumoh National Institute of Technology (kit) with GPA of 4.3/4.5
Certifications
(Expired in )
🐙 CAPA: Certified Argo Project Associate from The Linux Foundation
(Expired in )
🐳 CKAD: Certified Kubernetes Application Developer from The Linux Foundation
(Expired in )
🐳 CKA: Certified Kubernetes Administrator from The Linux Foundation
Skills
Here are my skills and highlighted items are industry-ready.
- MLOps & LLMOps
- Keywords:
- DevOps
- Keywords:
- GitOps
- Keywords:
- Application Development
- Keywords:
- Programming languages
- Keywords:
- Tools
- Keywords:
- OS and Hardware
- Keywords:
Interests
- Edge
- Keywords:
- Cluster Optimization
- Keywords:
- CNCF Projects
- Keywords:
Languages
- Korean
- Fluency: Native
- English
- Fluency: Working Proficiency
