AI Engineer (Korea)

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. Over 50 of the top 100 accounting and consulting firms trust Fieldguide to power mission-critical work.

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 OwnBuild 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
• Build evaluations and feedback loops to improve agent outputs
• Write prompts and retrieval pipelines that perform at scale

Contribute to Product Impact

• Work closely with senior engineers and product managers to scope and deliver features
• Translate customer workflows into clear engineering 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

ExperienceWe 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