AI Agent FAE — Edge AI Industrial Computing

Build the agents. Learn the platform. Two skill trees, one role.

We are an edge AI industrial computer (IPC) provider, and we are rebuilding how technical support works — not by hiring more people to answer tickets, but by building AI agents that do the repetitive work so engineers can focus on the hard problems. This is a junior role, but not an ordinary one: you are hired to help build that agent layer, while learning the edge-AI-IPC domain that makes the agents actually useful.

You don't need years of FAE experience. You need to be someone who, when they see a repetitive task, wants to automate it — and who is genuinely curious about how AI runs on real hardware at the edge. We'll teach you the domain; you bring the builder's instinct.

Why the two skill trees matter: an AI agent is only as good as the domain knowledge behind it. A ticket-triage agent that doesn't understand what a BSP is, or why a Jetson thermal-throttles, is useless. So you'll grow both at once — writing agents AND learning the platform they reason about.

What you'll do

◆Build and maintain AI agents in our support workflow — ticket auto-classification, knowledge-base retrieval (RAG), draft-response generation, and the automated test bench that turns debugging rules into executable checks.

◆Script against LLM APIs (Python + Anthropic / OpenAI SDK) and wire up tool-calling / MCP-style integrations that let agents act on real systems.

◆Learn the edge AI IPC domain hands-on: what our industrial computers do, how NVIDIA Jetson / x86 edge platforms are deployed, and the common failure modes customers hit — so the agents you build reason about real problems.

◆Continuously feed resolved cases back into the knowledge base — every fix you or the team makes becomes training signal for the agents.

◆Sit alongside senior FAEs as they debug real customer issues, turning their judgment into agent logic and test rules.

Requirements (no years-of-experience requirement)

◆STEM background, or equivalent self-taught proof — side projects, maker builds, GitHub, course records all count.

◆You already build with AI, not just chat with it — you've scripted against an LLM API, built a small agent/bot, automated something in your own workflow, or made a RAG demo. Show us. This is the one hard requirement.

◆Basic Python and comfort with the command line / Linux.

◆Follow-through: you finish what you start. Bring one real example of something you carried from zero to done — any field.

◆Genuine curiosity about hardware and edge computing — you want to understand the machine, not just the software on top of it.

Nice to have

◆Prompt engineering for structured / reliable LLM output; experience with LangChain, LlamaIndex, or agent frameworks.

◆Hands-on hardware (Arduino / Raspberry Pi / Jetson Nano — toy-level is fine), or any embedded Linux exposure.

◆Basic understanding of edge AI concepts: model deployment, inference, CUDA / TensorRT (even just from a course or side project).

◆Any record of carrying responsibility: led a project, shipped something people used.

◆Reading-level English (technical docs, code comments, customer tickets).

What we offer

◆A rare combination: you learn to build production AI agents AND gain deep edge-AI-IPC domain expertise — two of the most valuable skill trees in tech, at the same time.

◆A complete AI toolchain and a knowledge base that compounds — you build on a working system, not a blank page.

◆A growth path we can put in writing: contributing agents + handling L1 tickets in 3 months → owning an agent workflow + a platform specialty in 12 months → Senior AI/FAE hybrid track.

◆One-on-one senior mentorship on both the AI side and the hardware side.