About

AI Product Engineer Growing Out of Games, Research, and Real Systems

AI Product Developer @ Supercent
Ph.D. Candidate in Game Engineering @ Hongik University
8 years across NCSOFT + Com2uS

Curiosity -> Retrieve -> Innovation

I started in games because games force systems to be interactive, measurable, and brutally honest. If the AI is weak, players feel it. If the pipeline is slow, the team feels it. That environment shaped how I still build today.

My work began with game AI, procedural systems, digital humans, and computer-vision-based QA. Over time, that naturally expanded into a broader question: how do you turn research and experimentation into AI products people can actually use? That question is what now drives my work as an AI product engineer.

I still like the same loop:

Start with a hard question.
Retrieve signal from papers, product constraints, user behavior, and experiments.
Turn that signal into a system that can survive production.

What I Build Now

I am currently focused on AI products that need more than a good demo. I care about systems that can use tools, work with multimodal inputs, stay inspectable under failure, and improve through evaluation rather than intuition alone.

That means spending time on problems like:

agent workflows that combine reasoning, tool use, and clear product boundaries
multimodal QA pipelines that read gameplay, images, or video and turn them into useful reports
harness engineering, tracing, and human-in-the-loop checks for agent reliability
product surfaces where AI feels legible, not magical
the overlap between interactive systems, wellness, robotics, and AI-native user experiences

Why This Path Makes Sense

My background is not separate from this work. It is the reason I fit it.

At NCSOFT and Com2uS, I worked on game AI, balancing agents, procedural generation, and automation problems where feedback loops were tight and product quality mattered every day. In graduate research, I kept moving toward systems that observe what happened on screen, interpret it, and generate useful action from it. That line continues directly into current work on AI products, agents, and multimodal workflows.

The common thread is simple: build systems that can perceive context, make useful decisions, and help teams move faster without losing control.

Current Direction

At Supercent, I am continuing that shift from specialized game AI into broader AI product development. Publicly visible work and activity around my profile also reflect where I am spending time now: context engineering, harness engineering, MCP-connected tools, local and open-model experiments, and practical multi-agent orchestration.

I am especially interested in the part of the ecosystem that is becoming more real and less theoretical:

production agent stacks built around tools, tracing, and evals
MCP as a standard way to connect models to tools, data, and workflows
A2A-style interoperability for agent-to-agent collaboration
interfaces that keep human review in the loop for higher-risk actions

I prefer starting with one capable agent and adding orchestration only when the problem truly earns the extra complexity.

Selected Work

AI Product Developer, Supercent
Current work centered on AI product engineering, agentic workflows, and practical product delivery.

Ph.D. Candidate, Hongik University
Researching game engineering with a focus on AI engineering, multimodal QA automation, and production-minded applied research.

Recent research and publications

IEEE RAAI 2024 poster on image-based game QA automation
2025 publication on automated game QA reporting based on natural language captions
patents in emotional computing and game difficulty determination

Previous industry path

AI Programmer, NCSOFT
Game Programmer, Com2uS

This path gave me a strong bias toward systems that are measurable, iterative, and useful under real constraints.

How I Work

I prototype early to learn where the real constraints are.
I use research to sharpen direction, not to avoid shipping.
I treat evaluation, traces, and failure analysis as product work, not cleanup.
I care about clear interfaces because trust in AI systems is a UX problem as much as a model problem.
I like fast feedback loops, small experiments, and systems that get better with use.

Personal Activity

Outside formal work, I actively write and experiment in public.

I run this AI tech blog as a place to connect research, tooling, and product practice.
I have been exploring wellness, robotics, and AI through Wellflix and adjacent personal projects.
I regularly study and document topics like Claude Code, Codex, context engineering, MCP, ACP, LSP, harness engineering, and long-running agent workflows.
I also build and test multi-agent prototypes with local models, semantic routing, query expansion, and retrieval-heavy workflows.

This site is where those threads come together.

Tech I Reach For

Languages & ML Python | C | C++ | C# | PyTorch | TensorFlow | OpenCV

Interactive Systems Unity | Unreal Engine

Links

If you are building AI products that need to be grounded in real workflows, multimodal inputs, or interactive systems, I am always interested in comparing notes.