Software Engineer, AI Agent

About the role

You'll own end-to-end delivery of core AI agent capabilities, from architecture to production, driving excellence across orchestration, prompt engineering, and platform scalability. You'll mentor the engineers around you and set the standard for AI-native development. This role is built for someone who moves fast, thinks in systems, and uses AI to ship at a pace that surprises people. You might be this person if: You've owned a system that broke in production, made it better, and still think about why it broke. You use agentic tools like Claude Code or Codex as a core part of your dev workflow, not as a novelty. You enjoy mentoring junior engineers not because it's expected, but because you genuinely care about how they grow. You've shipped LLM-powered features into production and have strong views on where prompt engineering ends and architecture begins. You've led technical planning in a cross-functional setting and can hold your own in a room with PMs, designers, and engineers at once. You need to have: 3+ years of software engineering experience with direct ownership of AI/ML or backend systems in production. Deep Python expertise with a proven track record of building and scaling reliable, performant systems. Hands-on experience with LLM APIs. Strong system design and architecture skills, including distributed systems and cloud-native deployments on AWS or GCP. Strong English proficiency. You can communicate complex technical ideas clearly in writing and in conversation. What you'll actually do: Design and implement core agent features including advanced RAG pipelines, multi-step tool use, and agent orchestration logic. Drive architectural decisions for the AI agent platform with a focus on fault tolerance, scalability, and extensibility. Build and optimize evaluation, observability, and classification pipelines that improve agent performance over time. Lead prompt engineering strategy for diverse customer use cases, including designing A/B testing and automated regression pipelines. Improve retrieval logic and embedding models to push RAG pipeline performance further. Monitor AI research and actively bring relevant techniques into production systems, using agentic CLIs and automation tooling to accelerate iteration cycles. Guide junior engineers and interns through code reviews, architectural discussions, and technical planning. Partner with PMs, designers, and sales engineers to translate real customer needs into well-scoped technical solutions. Added Value: Prior experience with real-time or conversational interfaces. Hands-on experience with agentic frameworks such as LangChain or LangGraph. Familiarity with vector databases, search technologies, or large-scale data processing. Contributions to open-source LLM or AI tooling ecosystems. Native-level English fluency. Our KR benefits include (but are not limited to): Silicon Valley's equity program (1-year cliff) Hybrid work policy, flexible work hours Be Your Best Self: 3.9 million won (prorated by start date) for expenses ranging from professional development classes and training, to personality assessments, to gym memberships, to books, to fitness classes, to mental health services, to massages Learn a Language benefit - up to 3.6 million won per ye