Senior Applied AI Engineer: AI Features & RAG Systems

Pleo; España; Presencial; EUR 70.000 - 110.000; Jornada completa

Descripción de la vacante

Pleo is seeking an AI-powered engineer to shape AI-driven product features for our spend-management platform, collaborating with software engineers, data engineers and data scientists to move ideas from prototype to production for 40,000+ customers.

You will own the AI-product lifecycle, design end-to-end RAG retrieval pipelines, implement evaluation frameworks, and ensure production observability, collaborating across Product, AI Platform and Data teams to deliver reliable, scalable solutions.

Formación

• Proven experience shipping customer-facing AI features using LLMs in production.
• Deep expertise designing RAG systems and retrieval pipelines with embedding models and vector databases.
• Strong Python engineering and production-grade API development.

Responsabilidades

• Build and ship AI-powered product features for Pleo’s customers end-to-end.
• Design and build end-to-end RAG systems for product use cases.
• Own the AI-product lifecycle including prompt design and deployment.
• Build evaluation pipelines to verify AI feature quality.
• Instrument features for production observability and alerting.
• Collaborate with Product and Design to scope AI features from first principles.
• Partner with GenAI Platform to identify infrastructure needs.
• Support other engineers through reviews and mentoring.

Conocimientos

LLM features production

RAG system design

Evaluation frameworks for LLMs

Python engineering

APIs & data pipelines

Data quality reasoning

Agentic systems

Public cloud AWS/GCP

Kotlin & Python

Herramientas

Vector databases

Kotlin

AWS

GCP

Descripción del empleo

Pleo is seeking an AI-powered engineer to shape AI-driven product features for our spend-management platform, collaborating with software engineers, data engineers and data scientists to move ideas from prototype to production for 40,000+ customers.

You will own the AI-product lifecycle, design end-to-end RAG retrieval pipelines, implement evaluation frameworks, and ensure production observability, collaborating across Product, AI Platform and Data teams to deliver reliable, scalable solutions.