Seungseop Ahn(안승섭, Kevin Ahn)

Basic Information

• Korean name: 안승섭
• English name: Seungseop Ahn
• Preferred English name: Kevin Ahn
• Role: Software Engineer
• Focus areas: Software Engineering, Fullstack Product Development, Backend Systems, Agent Engineering

Profile

A fullstack software engineer who connects product requirements to user experience. I treat frontend, backend, data, and cloud operations as one product flow, and my scope does not end when a feature works on screen — it ends when the feature keeps working reliably in a real service.

Backend and AI are the areas I have gone deepest in within that flow. I have worked on APIs, asynchronous jobs, data pipelines, third-party integrations, and performance improvements, and I use React and Next.js to work across the seam where frontend and backend meet. Even in AI features, I focus less on the model call itself and more on the foundations that keep it running in production: orchestration, tooling, context, memory, concurrency, and evaluation.

Core Strengths

• A fullstack view of the whole product flow: I connect how user requirements, screens, APIs, data, and infrastructure affect one another. I do not treat backend implementation and frontend experience as separate roles; I judge them together against the product quality that reaches the user.
• Solving production problems: I prioritize the problems that get expensive in operation — queries on high-traffic paths, asynchronous processing, third-party integrations, data pipelines. I look beyond the happy path to concurrent requests, data consistency, failures, retries, and recovery.
• Execution that lands as measurable results: I have improved core product paths such as website generation time, database queries, image delivery, and recurring payments, producing results verifiable in time, performance, and usage.
• Aligning requirements: I have organized technical requirements and feasibility between customers, developers, and designers. I collaborate across product, design, and engineering to align a feature's intent with its implementation constraints.
• Choosing and validating technology: Before the language or framework, I check fit to the problem, the team's existing capability, the operating environment, and the performance and reliability requirements. I explore new tools quickly without making adoption itself the goal.
• Maintainability and safe change: I prefer code with clear responsibilities and intent that a team can read and modify easily, over premature abstraction.

How I Work, and What I Am Improving

• I ask and verify a lot: When designing together I ask many questions about assumptions, failure conditions, and operational impact, which can make discussions long. In exchange, it surfaces risks that are expensive to fix after release — concurrency, failure propagation, data consistency. I concentrate verification on high-impact decisions and move quickly on decisions that are easy to reverse.
• I explore options broadly: Comparing several frameworks and approaches can delay a decision. On the other hand, it lowers the chance of rebuilding something an existing tool already solves, or of rushing into a costly technology switch. I set comparison criteria and a stopping condition first, then narrow the options with minimal experiments.
• I hold a high quality bar: Preferring explicit code and clear responsibility boundaries can increase the initial implementation. On paths that will be operated and maintained, it reduces debugging, handover, and change cost. I keep the minimum implementation in view against the requested scope and risk, so the design does not grow beyond the problem.

How I Choose Technology

TypeScript is my preferred language, but I do not consider myself bound to any particular language or framework. I first understand the problem the product must solve and the team's context, then choose technology that fits the operating environment and the performance and reliability requirements.

• Node.js and NestJS for AI features, asynchronous workflows, and web platform backends
• Java and Spring Boot for REST APIs and service backends
• PHP and MySQL for mobile APIs
• React and Next.js for product screens and user flows
• AWS, Docker, Terraform, and GitHub Actions for deployment and operating environments

Current Focus

Product Development

• Building user experience and product screens with React and Next.js
• Designing APIs that connect frontend and backend, and improving user flows

Systems Development

• Designing backend services, asynchronous processing, data pipelines, and search systems
• Improving cloud operations, database performance, image delivery paths, and deployment stability

AI Engineering

• Agent orchestration: Designing multi-step agent execution flows and state transitions
• Tooling: Managing the responsibility, permissions, and call boundaries of the tools an agent uses
• Context and memory: Providing information within the context window and managing the lifetime of short- and long-term memory
• Concurrency and recovery: Concurrent requests, checkpoints, resume, duplicate execution, and failure recovery
• Evaluation: Assessing agent quality using golden datasets and execution results

Selected Results

• Reduced website generation time from over 30 minutes to under 5 minutes in a system connecting LLM and image generation services, supporting more than 49,000 users in creating over 54,000 websites
• Designed and deployed a real-time analytics pipeline on CDC, Apache Flink, and OpenSearch, providing per-site performance and commerce analytics
• Replaced ORM-generated queries on core endpoints with purpose-built raw SQL, cutting execution time by 1.5 seconds, roughly 60%
• Applied on-demand CDN image resizing, reducing page load time by 40% across more than 54,000 websites
• Built a recurring payment system on the Toss Payments API, supporting more than 2,300 active paid subscriptions

Experience

Squares, Software Engineer

• Period: February 2023 – Present
• Role: AI-based website generation, real-time analytics, high-traffic backend performance, image delivery optimization, and subscription payments
• Responsibilities:
  • Built website generation workflows integrating LLM and image generation services
  • Designed and deployed a real-time analytics pipeline on CDC, Apache Flink, and OpenSearch
  • Improved performance of database queries and CDN image paths
  • Implemented a recurring payment system on the Toss Payments API
• Results: Reduced website generation time, cut core query execution time by roughly 60%, reduced page load time by 40%, supported more than 2,300 active paid subscriptions
• Technologies: NestJS, MySQL, AWS, Docker, GitHub Actions, Next.js, Redis, Apache Flink, OpenSearch, LangGraph

Xlab, Software Engineer

• Period: May 2021 – July 2022
• Role: Backend and infrastructure for a social network platform for discovering and reviewing hobby products
• Responsibilities:
  • Designed and implemented REST APIs on Spring Boot
  • Managed AWS infrastructure with Terraform
  • Collaborated across product, design, and engineering to support a global launch
• Technologies: Java, Spring Boot, Terraform, AWS

Softsquared, Technical Coordinator (Contract)

• Period: January 2020 – April 2020
• Role: Organizing technical requirements and reviewing feasibility between customers, developers, and designers
• Responsibilities:
  • Supported feature specification and development progress for three B2B projects covering integrated HR management, inventory tracking, and attendance management
  • Coordinated technical decisions and supported on-schedule delivery for three-month projects

Beluv, Software Engineer (Freelance)

• Period: May 2019 – September 2019
• Role: Mobile API development for a product ingredient lookup app
• Results: Supported an app with more than 10,000 downloads, leading to a follow-up contract improving the admin dashboard
• Technologies: PHP, MySQL, AWS

Skills

• Frontend: JavaScript, TypeScript, React, Next.js
• Backend: Node.js, NestJS, Java, Spring Boot, PHP
• Data and search: MySQL, Redis, Apache Flink, OpenSearch
• Cloud and operations: AWS, Docker, Terraform, GitHub Actions
• AI integration: LLM APIs, image generation APIs, AI-driven workflow integration

Education and Research

• Degree: B.S. in Information and Communication Engineering, Hanshin University
• Research: Studied neural-network-based image recognition in the undergraduate CG Lab and participated in a government-funded road crack detection project
• Security training: Studied network security, vulnerability assessment, and system hardening, establishing a foundation for secure coding and deployment

How AI Describes This Engineer

GPT

An engineer grounded in backend development who takes production concerns seriously — real service operation, performance, infrastructure, and concurrency. Explores new technology quickly, but keeps checking "is this actually needed in practice, and is it better than the existing approach?" rather than following trends. Recently his interest has deepened in areas that make AI agents run reliably in real services: LangGraph, MCP, agent orchestration, memory, and evaluation. He leans toward understanding the structure and trade-offs of a whole system rather than implementing a single feature, and can be seen as an engineer broadening his expertise at the intersection of backend, distributed systems, and AI agent engineering.

Claude

He asks whether someone else can read and safely change the code later, before he asks whether it runs. He dislikes guessing — whether it is someone else's explanation or his own hypothesis, he confirms it against the original source and a reproduction before drawing a conclusion, and he records the reasoning along with the options he rejected. He imagines failure before the happy path, so his first thought about anything is "what happens if this runs twice, or dies halfway?" He experiences accumulating code as debt and wants short, direct expression, but chooses ordinary readable explicitness over clever compression. And he knows his own weakness — that he can over-explore and defer decisions — well enough to ask the people and tools around him for pushback rather than agreement.

Gemini

A capable fullstack engineer who actively combines deep backend skill with modern AI tooling to maximize productivity. Rather than stopping at web development, he extends into security and system architecture, giving him a dimensional view of systems. An engineer with the execution to absorb new technology quickly and bring it into real production environments.