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! 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 one of the first hires in our Data & AI Products 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

As a Staff Applied AI Engineer you will:

Build and ship multiple AI-powered product features, setting the bar for delivery at Pleo, across agentic workflows, spend intelligence, automated actions, and more.
Bring deep applied AI expertise and strong judgment on trade-offs (quality vs latency/cost, build vs buy, agent patterns vs simpler approaches) and help teams avoid hype-driven decisions.
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.
Act as a design partner to the GenAI Core platform team: challenge decisions with evidence from real feature delivery, bring clear requirements, and validate platform choices in production.
Establish and enforce practical standards for AI feature delivery in product squads (evaluation strategy, monitoring expectations, safe prompt/versioning practices, privacy & safety guardrails) using GenAI Platform tooling (not building the platform itself).
Upskill the team through mentorship, reviews, pairing, and lightweight playbooks that make other engineers faster.

What you bring

You will thrive in this role if you have:

Proven experience shipping multiple GenAI features into production, 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.
Deep applied AI judgment: you can reason about evaluation, retrieval quality, tool use, failure modes, and what is/isn’t worth building.
Experience applying evaluation and observability to LLM systems (tests, golden sets, online metrics, monitoring) and using those signals to iterate.
Strong understanding of privacy and security concerns when building LLM applications: prompt injection, handling PII and data leakage risk.
Solid experience with the modern AI stack including Vector DBs, orchestration and durable execution frameworks, and LLM APIs.

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Staff Applied AI Engineer at Pleo