AI Engineer (Korea)

Requirements

• ABOUT THE ROLE Fieldguide is building AI agents for the most complex audit and advisory workflows.
• We’re a San Francisco-based vertical AI company operating in a $100B+ market undergoing rapid transformation.
• As an AI Engineer, you’ll build Fieldguide’s intelligence layer—agentic workflows, architectures, and evaluation systems that power enterprise-grade agents.
• You’ll operate at the intersection of product engineering, applied AI, and production systems.
• WHAT YOU’LL OWN BUILD AND SHIP AI AGENTS - Implement agentic workflows that automate complex audit tasks - Turn customer problems into discrete, testable agent behaviors - Integrate LLMs, tools, and retrieval logic into reliable agent experiences - Monitor and maintain agents in production with a focus on reliability and performance EXECUTE WITH AI-NATIVE LEVERAGE - Use AI tooling to accelerate how you design, build, and test features - Prototype quickly and harden systems for enterprise reliability -
• requirements - Identify and surface capability gaps to improve team velocity WHO YOU ARE You’re a strong software engineer who’s built your skills for an AI-native world.
• These principles resonate with you: - Bias to building: You move fast and resolve uncertainty by shipping - AI-native instincts: You're excited to use LLMs, agents, and automation as core tools - Curiosity and learning velocity: You ramp quickly on new problems and technology - Collaborative: You communicate proactively and seek feedback often - Attention to detail: You care about quality in code and customer-facing outputs
• EXPERIENCE We care more about capability and trajectory than years on a resume, but most strong candidates have: - 1–3 years shipping production software in real-world systems - Proficiency in TypeScript and/or Python - Some exposure to LLM APIs (OpenAI, Anthropic, Gemini) or AI tooling - Familiarity with retrieval pipelines, RAG concepts, or vector databases is a plus -
• Experience with React and Postgres is helpful but not required - Comfortable working in ambiguity with direction from senior engineers WHAT SHOULD EXCITE YOU - Enterprise-grade reliability: Building systems professionals depend on - Human-in-the-loop design: Knowing when to automate vs.
• when to surface decisions - Nuanced evaluation: Audits require judgment, so feedback structures matter - Explainability: Making AI outputs and reasoning transparent and trustworthy - Complex domains: Navigating compliance and enterprise rigor while moving fast - Shipping daily value: Delivering agent experiences customers use every day