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Jinyoung
Dev

AI Product Engineer · Swingvy

Jinyoung Park / Software Engineer

Portrait of Jinyoung Park

Building with AI.
From idea to product.

Planning, architecture, development, QA, and deployment — across the product lifecycle.

I’ve built and operated backends and cloud infrastructure for games and HR services. Today, I bring that experience to building products with AI agents.

01A deliberate shift

I changed how I work. Then I changed my role.

In late 2025, I began studying AI-assisted product development and expanding my responsibilities. I wanted to work across the entire product lifecycle. Through discussions with my company, I transitioned from Back-end Engineer to AI Product Engineer.

With a wider scope, defining what to build, structuring the work, and verifying results became more important. I now build products with AI agents, drawing on my experience developing and operating services.

  1. FOUNDATION

    Backend & infrastructure

    Experience building and operating services

  2. LATE 2025

    A self-initiated shift

    Learning AI product development and expanding my scope

  3. NOW

    AI Product Engineer

    A new role, agreed with my company, across the product lifecycle

02How I work

More AI. More judgment and verification.

01 /

Define what to build

I clarify requirements and constraints, then structure the work in detailed documents. I invest in making the scope and context clear for AI agents.

02 /

Build verification into the work

I use AI for code generation and testing, while considering the impact on existing systems and how to evaluate the quality of the result.

03 /

Start with what matters. Improve.

I prioritize delivery, operational effort, and cost. I’ve integrated structural improvements into feature work and evolved infrastructure in stages.

03Selected work

The work behind my judgment.

Evaluating new technology, improving architecture alongside feature delivery, and evolving the systems that run the product.

01 / AI Q&A · Swingvy · 2024

Finding the balance: RAG quality and cost

  • RAG
  • User Feedback
  • Model Evaluation

I built a Q&A system based on Singapore’s labour laws when LLM capabilities and available resources were limited. Answer quality, latency, and cost all mattered.

THE APPROACH

I refined retrieval and built a foundation for collecting user feedback on answer quality. I also created an evaluation process to compare models on response speed, quality, and cost.

02 / Backend · Swingvy

A module migration, built into feature delivery

I incrementally extracted leave management from a monolith into an independent module, organizing the work around the feature delivery schedule.

  1. 01Feature planning
  2. 02Define boundaries
  3. 03Build + migrate
  4. 04Shared QA

THE APPROACH

During feature planning, I identified boundaries based on related functionality and potential side effects. Migration stories joined feature epics, so feature QA also covered the migrated behavior.

  • Spring Boot
  • JPA
  • Spring Cloud
  • TDD

03 / Infrastructure · Swingvy

Launch first. Keep evolving operations.

I built and operated AWS and GCP infrastructure, prioritizing early delivery and improving the environment over time with cost and operational effort in mind.

  1. EC2Direct deployment
  2. OpsWorks / ChefInfrastructure automation
  3. ECSAll services migrated

THE APPROACH

I moved from direct EC2 deployments to automation with OpsWorks and Chef. As OpsWorks support ended and management overhead grew, I migrated all services to ECS.

  • AWS
  • GCP
  • ECS
  • Chef
  • Docker

04Experience

Experience built. Responsibilities expanded.

  1. SWINGVY

    2016.10 — Present

    Back-end Engineer → AI Product Engineer

    Built and operated HR backends and cloud infrastructure. Now working across the product lifecycle with AI agents.

  2. LINE PLUS

    2014.02 — 2016.10

    Back-end Engineer

    Developed backend APIs for LINE Rangers, a mobile game with over 10 million downloads.

  3. NAVER

    2013.06 — 2013.12

    Intern

    Started my engineering career as an intern.

05Tech Stack

Tech stack

From tools for building products with AI to the backend and infrastructure that keep them running.

  • Claude Code
  • Claude
  • Gemini
  • Langchain
  • Python
  • FastAPI
  • JavaScript(TS)
  • React
  • Next.js
  • Tailwind CSS
  • Vercel
  • Java
  • Kotlin
  • Spring Boot
  • AWS
  • GCP
  • Docker
  • PostgreSQL
  • MySQL
  • Redis
  • Git
  • GraphQL

LET’S BUILD TOGETHER

Have a product to build?Let’s talk.

I’m interested in AI-assisted product development, backend and infrastructure challenges, and teams thinking about all three.

I welcome hiring conversations and technical discussions.