AI Engineer

AI Engineer Role Overview We’re looking for an AI Engineer who is, first and foremost, a strong software engineer someone with real backend fundamentals who then uses AI agents, LLMs, and tools like Claude as a force multiplier for full-stack, AI-assisted development . We are still software engineers. AI accelerates how we build; it does not replace knowing how to build . The wrong candidate is an “AI-only” profile who can prompt but can’t reason about a data model, a transaction boundary, or a race condition. The right candidate could hold their own on the engineering team without AI in the room and is exceptional with it, shipping across the full stack faster than a traditional workflow allows. You’ll work on a modern hospitality platform: a NestJS / TypeScript API backed by MariaDB , with a React / TypeScript frontend, built with a database-first architecture and heavy use of AI-assisted development workflows.

Desired Candidate Profile

Strong software engineer with backend depth (the core requirement). Solid command of fundamentals API design, service architecture, layered/clean design (controller service repository), separation of concerns, concurrency, error handling, testing, and performance. Writes production-grade code that is correct, maintainable, and holds up under change. This is the non-negotiable foundation everything else sits on. Real database competence. Comfortable with schema design, query optimization, transactions and isolation levels, indexing, stored procedures, and data modeling. Understands why a query is slow or a write is unsafe not just how to make the error disappear. Fluent in full-stack, AI-assisted development. On top of strong engineering, knows how to drive agentic workflows with Claude and LLMs across the whole stack backend, frontend, and data layer. Decomposes work, orchestrates tools, structures context, and validates output. Picks the right model and pattern for the job, and knows where AI genuinely helps versus where it introduces risk while always owning the correctness of what ships. Deeply curious about what happens behind the scenes (the differentiator). Doesn’t stop at “it works.” Wants to understand the mechanics on both sides how the runtime, framework, ORM/driver, and data layer actually behave underneath the abstractions, and how tokens, context windows, tool-calling, retrieval, and model behavior work under the hood. This is what separates an engineer who can fix a system from one who can only use it. We value the why over the that . Strong debugging instincts. Given a failure, a stack trace, or “it works locally but not in prod,” reproduces, isolates, and diagnoses methodically forming hypotheses and following evidence to root cause instead of guessing. Genuinely eager to learn. The landscape shifts monthly. Stays curious, absorbs new tools and techniques quickly, and treats unfamiliar problems as a chance to level up.