LEAD AI/ML ENGINEER

Responsibilities

• Architecture of AI systems: from selecting LLM models to building RAG pipelines and integrating computer vision — all key technical decisions are yours.
• Quality of recommendations: we do not do proof-of-concept — each iteration must increase conversion. You are responsible for ensuring AI truly helps people find the perfect vacation.
• Rapid iterations: we roll out changes every week, collect metrics, analyze, and improve. An ideal environment for those who want to see the results of their work in real-time.
• Experiments: A/B testing of prompts, hybrid search (text + visual + reviews), multimodal models — you decide what to test and quickly validate hypotheses with real users.
• Scaling: starting with 2000 tours to the Maldives, by the end of the year processing 50,000+ tours across three directions with thousands of requests per day.

Requirements

• Experience with production LLM applications: you have built RAG systems or chatbots based on GPT/Claude and know all the pitfalls.
• Expert-level prompt engineering: you can extract maximum from models through proper prompt structure, few-shot examples, chain-of-thought.
• Experience with embeddings and vector search: you understand how similarity search, hybrid search works, when reranking is needed.
• Production-level Python: you write clean, fast code that is easy to maintain.
• Product mindset: you understand that AI is a tool for solving business problems, not an end in itself.
• Ability to work with metrics: conversion, session time, escalation rate — these are your main KPIs.
• Quick decision-making: better to roll out an imperfect solution in a week and test it than to spend a month making it perfect.
• Ability to explain complex concepts in simple terms: you need to convey to the team and business why we choose a particular architecture.
• Will be a plus:

- Experience with computer vision / multimodal models (CLIP, GPT-4o, Claude 4.5 Sonnet). - Experience with high-load systems and latency optimization. - Understanding of recommendation systems. - Understanding of MLOps: model monitoring, versioning, A/B testing. - Experience building recommendation systems. - Experience building MCP for LLM systems. - You have used LLMs to automate your tasks and know what works and what doesn't.