Confirmed on the employer's own hiring board on Oct 8, 2026. First seen by Alion on Sep 24, 2026.
Join our team as an AI Engineer, where you'll be at the forefront of developing innovative AI solutions for the restaurant industry. You'll work on document understanding, conversational agents, end-to-end workflows, product and format matching, and more. Your expertise in AI and engineering will help us scale our solutions and solve real customer pain points.
Missions
- Architect and ship AI solutions for restaurateurs, balancing latency, cost, and accuracy.
- Own AI observability end to end: monitor billions of traces, design evaluation frameworks, and close the improvement loop as fast as possible.
- Build the harness. A model on its own is not a product. The tools it can call, the data it can read, the guardrails, the place where a human steps in.
Profil recherché
- You've worked with an eval system to measure and improve model performance, rather than a handful of test prompts and a good feeling- You combine engineering rigor with product thinking. You design, debug, and improve end-to-end systems that solve real problems
- You're truly AI-native: you follow new models, papers, and techniques, and you continuously refine how you use them in practice
- You're also AI-native in how you build: you use modern AI-powered dev tools (Claude Code, Cursor, Codex or similar) and assemble your own agents and workflows to move faster
- 2+ years of hands-on experience with GenAI and 3+ years of engineering experience overall. One year of very intense AI work also counts, if it's backed by solid software or data science experience
- Fluent in Spanish and comfortable in English
- You've run GenAI in production at real scale. As a reference: an agent or workflow doing at least 5,000 executions a month, and you were the one accountable for it. Experiments don't count
- AI observability platforms like langfuse (tracing, metrics, evals for LLM apps and agents)
- Designing and running evaluations
- Multimodal LLMs, especially vision models for OCR document understanding
- Building and orchestrating multi-agent systems
- RAG systems and vector databases
- Designing or working with MCPs

