Senior Applied AI Engineer
LisbonAll locationsLisbonLondonCopenhagenMadrid Remote
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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. Please note: applications will be open until 1st September 2026 at 09.00 CEST. We will not review any application before the window is closed so please take all the time you need to submit a great application! You are primarily interested in model research or algorithm development. This role is about shipping working AI features to production. Transparency is important to us so we also wanted to share some insights about what we’re looking for in applications to ensure you can set yourself up for success! Profile to role fit: this is neither a research-driven nor a traditional data or software engineering role. We're looking for someone with proven experience shipping customer-facing AI products who can also confidently operate at Senior level in a SaaS organisation. This means we'll be looking for evidence that you can own fairly complex technical features from design to implementation.
What you'll 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.