My background started in Visual Communication / Animation at Hongik University Sejong Campus. Later, I added Computer Science as a double major. While working on pipeline and render farm projects, I became more interested in the systems behind the tools: servers, shared storage, logs, worker status, and failure handling.

My long-term goal is to become a DevOps engineer. Right now, I am trying to build that foundation through SRE-style operational experience: checking logs, understanding infrastructure components, monitoring system state, and writing down how to respond when something breaks.

1. Deadline Render Farm Automation
   On-premise render farm automation project with PySide UI, MongoDB reservation/auth, Deadline job submission, NAS workflow, and troubleshooting documentation.
   Main public source snapshot: src/

• Linux operation and shell scripting
• AWS infrastructure basics: VPC, EC2, ALB, Security Group, IAM, CloudWatch
• Monitoring, logs, job/worker status, and troubleshooting
• Deployment flow and operational documentation
• Long-term interest in semiconductor IT / FAB infrastructure operations

Built and operated an on-premise render farm environment using AWS Thinkbox Deadline in a university lab.

• Used around 20 lab PCs, a server PC, NAS shared storage, and DCC render licenses.
• Built a PySide UI so students could submit and check render jobs more easily.
• Checked job status, Worker status, failed jobs, error codes, logs, and MongoDB job data.
• Troubleshot Worker connection issues, NAS path issues, port/firewall settings, and license problems.
• The tool was used by around 40 students during the graduation project period.
• The project is now in its first year of continued operation and is being extended with long-term lab usage in mind.
• Expansion to other university lab environments has been decided.
• Tested Ansible during the project, but it was not part of the final core setup.

This was not an AWS cloud production deployment. It was an on-premise render farm infrastructure project using AWS Thinkbox Deadline.

I am currently practicing AWS infrastructure setup while focusing on how each component works and how to check problems.

• VPC, public/private subnets, routing, Security Groups
• EC2, ALB
• IAM basics
• CloudWatch monitoring and log checking
• Manual setup practice to understand the role of each component

A Python / PySide-based VFX pipeline tool project.

• Worked on login, loader, publisher, and saver UI flows.
• Handled Maya/Nuke environment setup and Linux .desktop launch flow.
• Looked into ShotGrid API integration patterns.
• Focused on making the tool structure easier to run, inspect, and maintain.

During the Netflix Academy bootcamp and university projects, I worked on Linux-based pipeline automation using Python, Shell, and PySide.

• Connected DCC workflows, render submission, shared paths, and job metadata.
• Took a team lead role in a project.
• Worked on making the tool usable in an actual student production environment.

I have also worked on AI-assisted tools and web UI experiments, but they are not my main direction now.

• Lecture Companion Agent: PDF-based study note generation workflow
• Landing Agent Harness: landing page workflow prototype
• Capstone Repo Polisher: repository cleanup and documentation workflow

Programming Languages
C++, Python, C#, Bash / Shell Script

Operations / DevOps
Linux, Shell Script, Git, GitHub

Infrastructure / Cloud
AWS, VPC, EC2, ALB, Security Group, IAM, CloudWatch

Monitoring / Troubleshooting
Log inspection, job/worker status tracking, error checking, operational notes

Programming / Automation
Python, PySide6 / PySide2, JavaScript / TypeScript

Render Farm / Pipeline
AWS Thinkbox Deadline, MongoDB, NAS-based workflow, Maya, Blender, Arnold

I want to grow toward DevOps engineering by first becoming comfortable with real operation work: Linux servers, infrastructure setup, monitoring, logs, and troubleshooting.

I am also interested in infrastructure operation work in semiconductor IT / FAB environments, especially server and equipment monitoring, log analysis, incident response, and operation automation.