Applied AI Engineer

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’.

The word ‘Pleo’ actually means ‘more than you’d expect’, and living by that mantra has been the secret to our success over the last 10 years. 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! 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

This is not a solo end-to-end role. You'll work as part of a small, complementary data team — pairing with a Staff Data Platform Engineer and a Staff Data Scientist — each bringing distinct skills while you bring applied AI engineering and data context. Together, the team covers the full chain from data to shipped product. While part of the Data team and reporting directly into our Director of Data Product, you'll work embedded in product squads across the business.

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

What You’ll Be Doing

As a Staff Applied AI Engineer you will:

• Work alongside product engineers and data specialists to build and ship AI-powered products including spend intelligence, automated actions, agentic workflows, and more.
• Work directly with Product, Design, Engineering, and business stakeholders to advise and prioritise on what's actually worth building. You aren't just implementing specs, you're discovering and defining the product.
• Own the evaluation, monitoring, and operationalisation of AI features — setting up evals, tracking drift and performance, and managing prompt changes safely in production. You will help establish how Pleo does this at scale and own the development in production.
• Partner with our GenAI Core platform team to define and prioritise what tooling is being built for the whole company, and subsequently act as a champion for the use and adoption of our tooling.

What You Bring

You will thrive in this role if you have:

• Proven experience shipping GenAI features at scale in a customer-facing product. You've moved past prototype phases and ideally have experience shipping multi-step, tool-using agents in a user-facing product.
• The ability to translate complex business challenges and product visions into scalable AI solutions. In other words, you can autonomously scope, design and build.
• Experience setting up evals, monitoring, and production observability for LLM-based systems.
• Solid experience with the modern AI stack including Vector DBs, orchestration frameworks and LLM APIs.
• Experience building APIs, services and data retrieval pipelines (RAG, vector search...) to feed data into LLMs.
• Deep proficiency with Python for both data and software engineering, SQL and Big Query or similar major cloud providers.

To share extra context, our current tech stack includes GCP, Big Query, Airflow, Python, SQL on the Data side and AWS, Kotlin, Java script, Typescript on the Product side while our infrastructure is…