Senior Applied AI Engineer

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

About Pleo

Messy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. At Pleo, we're changing that. We build spend solutions that make managing money seamless, empowering, and surprisingly effective for finance teams and employees alike - with a vision to help all businesses 'go beyond'.

Now, we're at a pivotal moment in our journey; every move we make has a direct impact on our 40,000+ customers, our business, and our collective success. We need people who take pride in uncovering customer needs, who turn complex problems into simple solutions, challenge the way things are done (respectfully), and always aim high. With great ambitions driving us forward, we can't say we've got this whole thing figured out. And frankly, that's half the fun! What we can say is that we're a driven, progressive, and, importantly, a kind bunch of 850+ people from over 100 nationalities, all committed to delivering the future of business spending, together.

About The Role

This is an exciting opportunity to help shape how Pleo builds AI-powered product features, working alongside software engineers, data engineers and data scientists to take ideas from prototype to production. Pleo has over 40,000 customers and a decade of unique spend data — an incredible foundation to build on. Your mission will be to harness this data to create real product value.

Who You'll Be Working With And Reporting To

You'll be reporting to the Senior Manager for Data & AI Products and will be one of the first hires in the team but, rest assured, you will not be working solo! You will work very closely with other Engineers and Data Scientists, each bringing distinctive skills while you bring applied AI engineering and data context. Together, the team covers the full chain from data to shipped product.

Your core focus is on building and shipping AI-powered features, with a strong collaborative element across Product Engineering, AI Platform, and Data & ML Platform teams.

What You'll Be Doing

• Build and ship AI-powered product features for Pleo's customers, owning outcomes end-to-end for a scoped area.
• Design and build end-to-end RAG systems for specific product use cases: chunking strategies, embedding model selection, retrieval optimisation, and quality evaluation.
• Own the full AI-product lifecycle: prompt design, context and state management, agentic loops, output parsing, edge case handling, and safe production deployment.
• Build evaluation datasets and automated eval pipelines that give the team confidence in AI feature quality before and after changes.
• Instrument AI features for production observability: logging, drift detection, quality monitoring, and alerting.
• Collaborate with Product and Design to scope AI features from first principles; you are a co-author of what gets built, not just an implementer of specs.
• Partner with our GenAI Platform team to identify infrastructure needs and champion adoption of platform tooling.
• Support other engineers through reviews, pairing, and pragmatic technical leadership on the projects you lead.

What You Bring

• Proven experience shipping customer-facing AI features using LLMs; beyond prototype stage and into real production systems with real users.
• Deep practical experience with RAG system design and the full retrieval pipeline: embedding models, vector databases, chunking, hybrid search, re-ranking.
• Experience building and operating evaluation frameworks for LLM-based systems; you have a systematic approach to measuring quality.
• Strong Python engineering; your code is production-grade, tested, and maintainable.
• Experience building APIs and data retrieval pipelines that feed context into AI systems.
• Enough data intuition to reason about data quality, schema, and retrieval architecture without needing a dedicated data engineer beside you at all times.
• Experience with agentic system patterns: tool use, multi-step orchestration, error handling in LLM workflows.
• Familiarity with public cloud providers (AWS, GCP).
• We operate a polyglot platform, with components written in Kotlin and Python. While this role is Python-focused, you will need from time to time to contribute in a Kotlin stack, so being open to that is important.

Why is this role a good fit for you

• You have strong product instincts and build with the user in mind. You understand that a model is only as good as the problem it solves.
• You have moved past prototyping and have a deep understanding of the realities of LLMOps, data retrieval