What You Bring

You will thrive in this role if you have:

• 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

This role is a good fit for you if:

• 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, prompt and context engineering, as well as model evaluation in production.
• You don't just call APIs, you understand the data feeding the AI system and can reason about data quality, architecture, and retrieval without needing a dedicated data engineer beside you at all times.

This role is not a good fit for you if:

• You are primarily interested in model research or algorithm development. This role is about shipping working AI features to production.
• You need fully defined specs and a groomed backlog to operate effectively. We expect you to discover and define the problem alongside Product and Design.
• You cannot explain AI trade-offs clearly to non-technical stakeholders. You will regularly work with Product Managers, Designers, and business leaders who need to understand what is and isn't possible.

How You’ll Develop In This Role

In your first 6 months at Pleo, you’ll:

• Familiarise yourself with our codebase, tooling and roadmap.
• Partner with our Principal Engineer to define and own our approach to AI feature development.
• Contribute to shaping the roadmap for tooling and features developed by our GenAI Core team.
• Collaborate with Product and Data teams to ship a first feature to production

We’re committed to helping you develop your career, whether that means taking on bigger projects, stepping into leadership, or acquiring new skills!

The location

Please note: We can hire on a remote, hybrid or in-person set-up in any of the locations listed on the advert but you will need to be physically based in the country of your choice with a valid right to work.

We are unable to offer visa sponsorship for this role.

Show me the benefits!

• Your own Pleo card (no more out-of-pocket spending!)
• Lunch is on us for your work days - enjoy catered meals or receive a lunch allowance based on your local office
• Comprehensive private healthcare - depending on your location, coverage options include Vitality, Alan or Médis
• We offer 25 days of holiday + your public holidays
• For our Team, we offer both hybrid and fully remote working options
• We use MyndUp to give our employees access to free mental health and well-being support with great success so far
• Paid parental leave - we want to make sure that we're supportive of families and help you feel that you don't have to compromise your family due to work

The interview process

We want to ensure you are set-up for success and understand what will be expected of you. If your application is successful, our interview process is as follows:

• Intro call: A 30-minute chat with our Talent Partner to discuss the role and your background.
• Technical screening: A 15 to 30 minute call with our Talent Partner to check your knowledge of key technical topics
• System design interview: A 75-minute practical session with our engineers focusing on scoping and designing an AI feature
• Live coding interview: A 75-minute practical session with our engineers focusing on implementing your solution.
• Hiring Manager interview: A 60-minute conversation to deep dive into your knowledge and experience.
• Final interview: A leadership interview focusing on your behavioural, communication and collaboration skills.

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!

• CV writing and content: we receive a lot of CVs, and many of them are AI-generated. We love seeing people leverage AI—it’s a big focus for us internally too—but without human intervention, these CVs can sometimes become generic and fail to show a candidate in the best light. What we're really looking for is the specific details of real impact that only you know from your previous experience. A top tip from us is to use the “Achieved X, as measured by Y, by doing Z” formula (credit: Laszlo Bock, :2014) to give a really clear picture of what you’ve worked on. A final note: including links to your previous companies' websites is a huge help and allows us to truly understand your background!
• Application care: every single application we receive is reviewed by a human (yes, hundreds of them) because we believe that candidates' efforts should be matched by an equal level of human care. This means that we expect a similar level of attention put into your application. Read and answer the application questions carefully, they make a huge difference in our decision-making process.
• Profile to role fit: this is neither a research