What you bring

You will thrive in this role if you have:

• Strong backend/systems engineering background, with experience building and operating production services with reliability and observability requirements.
• Experience designing and delivering shared platform or infrastructure components used by multiple teams.
• Strong production ownership: monitoring, alerting, incident response, debugging, and post-incident learning.
• Distributed systems fundamentals, including async workflows, idempotency, consistency tradeoffs, and designing for failure.
• Hands-on experience with LLM APIs or strong interest in learning their production failure modes: rate limits, context windows, multi-vendor routing, latency variance, and cost control.
• Security mindset for AI systems, including prompt injection risks, PII in logs, data leakage, and safe credential handling.
• Strong programming experience in either a JVM-based language or Python. We operate a polyglot platform with components written in both Kotlin and Python, and you’ll be expected to contribute to both.
• Clear communication and collaboration skills, especially when working with product teams and other engineers to turn ambiguous platform needs into practical solutions.

Why is this role a good fit for you

This role is a good fit for you if:

• You are passionate about developer experience and get excited about being a force multiplier for engineering teams.
• 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 understand both the engineering and the data side of things, and are comfortable switching languages or technologies to achieve your goals.

This role is not a good fit for you if:

• You are primarily interested in model research or algorithm development. This role is about building the tooling that enables product teams to ship AI features to production.
• You prefer building customer-facing features. Your key users will be other Pleo Engineers.
• 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:

• Develop a clear picture of how AI features are currently being built at Pleo and where the biggest infrastructure bottlenecks are.
• Take ownership of a core platform component such as the LLM gateway, RAG infrastructure, MCP gateway, or evaluation framework, and improve its reliability, observability, or developer experience.
• Deliver production-ready improvements with clear rollout plans, monitoring, and operational documentation.
• Partner with Applied AI Engineers and product teams to identify platform investments that would accelerate their work.
• Contribute to Pleo's internal standards for AI feature development: how we evaluate quality, manage prompts, and monitor production AI systems.

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-driven nor a traditional data or software engineering role. We'