AI Engineer
Own how Booka uses AI — from leveling up our internal AI-native engineering practices to designing, shipping, and operating customer-facing AI in the product. You'll be the person who makes AI reliable, measurable, and genuinely useful.
About the role
We're looking for an AI Engineer to own AI as a capability across the company. On one side, you'll optimize how we already use AI in development and operations: better workflows, guardrails, evaluation, and quality bars for AI-assisted work. On the other, you'll integrate and own AI-powered features in Booka itself — the experiences that help independent beauty pros and their clients get more done with less friction.
You'll partner closely with product and engineering to turn ideas into production systems: the right models and APIs, thoughtful UX, observability, cost and latency discipline, and clear ownership as AI features evolve.
Responsibilities
- Lead the design and implementation of AI-powered product features end-to-end — from prototyping to production rollout and iteration.
- Improve how the team uses AI internally: standards for prompting, review, testing, and operationalizing AI-generated changes without sacrificing quality.
- Define and maintain architecture for LLM integrations: routing, fallbacks, context management, tool use, and safe handling of user data.
- Build evaluation and monitoring so AI behavior is measurable — quality checks, regression tests, tracing, and dashboards that surface drift and failures.
- Partner with engineering on performance and cost: caching, batching, model selection, and right-sizing infrastructure for AI workloads.
- Stay current on the AI landscape and bring pragmatic recommendations — when to ship with an API, when to fine-tune or add RAG, and when simpler heuristics win.
- Document patterns and playbooks so AI development scales beyond one owner.
Requirements
- Strong software engineering fundamentals — you've shipped and maintained production systems, not only notebooks or demos.
- Hands-on experience building features with LLMs (APIs, agents, or embedded AI) in a real product environment.
- Comfort owning reliability and UX for AI: error handling, loading and empty states, and clear behavior when models fail or refuse.
- Familiarity with evaluation, testing, or observability for AI systems — you can explain what "good" looks like and how you catch regressions.
- Ability to collaborate across product, design, and engineering; you translate fuzzy requirements into concrete technical plans.
- Pragmatic judgment on privacy, safety, and compliance as they apply to customer data and generative features.
Nice to have
- Experience with TypeScript/Node.js and modern web stacks (e.g. Next.js, React) aligned with how we build Booka.
- Experience with the Vercel AI SDK, AI Gateway, or similar unified LLM client patterns.
- Background in product analytics or LLM observability (e.g. tracing, cost attribution, experiment-driven iteration).
- Experience optimizing AI-assisted development workflows in a team setting — code review norms, CI checks, and human-in-the-loop quality gates.
- Familiarity with retrieval-augmented generation, structured outputs, or fine-tuning when they genuinely move the needle.