AI Product Engineer

Overview As AI Product Engineer at WeRoad, you will build and maintain LLM-powered features in production, including agents, classifiers, RAG pipelines, and AI search used by thousands of travelers. You will design LangGraph workflows, prompts, and tool-calling logic to convert business needs into reliable AI behavior.

You will set up evaluations, monitor quality, latency and cost with observability tools, and use data to drive improvements. You will integrate AI services with WeRoad's product and data stack, optimize token usage and model routing, and work with cross-functional teams to scale AI use cases from prototype to production.

You will own problems end-to-end, from understanding the need to? Retribuzione / Benefits Performance-based bonus a free WeRoad trip every year or travel discount trust-based holidays opportunities to learn new parent support hybrid working Responsabilità Build, ship and maintain LLM-powered features in production (agents, classifiers, RAG pipelines, AI search).

Design and iterate LangGraph workflows, prompts and tool-calling logic. Set up evaluations: define test sets, track quality, latency and cost with tracing tools (e.g., Langfuse). Integrate AI services with the product and data stack (APIs, databases, BigQuery, GCP).

Optimize token usage, model routing, prompt efficiency and response quality. Collaborate with Product, Customer Care, Marketing and Data to identify AI use cases and move them to production. Own problems end-to-end from understanding the need to shipping and monitoring the solution.

Requisiti fondamentali Product sense: focus on the user and impact, and know when not to use AI. Experience as software or product engineer in a product company or fast-growing startup. Agency and ownership mindset: take vague tasks and ship results with accountability.

Engineering skills for AI in production: reason about latency, cost, reliability, and scalability. Experience using TypeScript or Python is a strong plus. Have built AI-enabled solutions (work, research, or personal projects).

Measure before you trust: evaluate prompts/models before deployment. Clarity to explain technical choices to non-technical people. Fluency in English. ownership strong communication problem-solving mindset TypeScript Python LLM integration

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