Staff Applied AI EngineerPleoFull Time
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Staff 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 means more than you’d expect, and living by that mantra has been the secret to our success over the last 10 years.

We’re now 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. With 850+ people from over 100 nationalities, we’re 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. With over 40,000 customers and a decade of unique spend data, your mission will be to harness this data to create real product value.

You’ll be one of the first hires in our Data & AI Products team, collaborating closely across Product Engineering, AI Platform, and Data & ML Platform teams.

What You’ll Be Doing

  • Build and ship multiple AI-powered product features across agentic workflows, spend intelligence, and automated actions.

  • Bring deep applied AI expertise and strong judgment on trade-offs (quality vs latency/cost, build vs buy, agent patterns vs simpler approaches).

  • Work directly with Product, Design, Engineering, and business stakeholders to advise and prioritise what’s worth building.

  • Own evaluation, monitoring, and operationalisation of AI features: set up evals, track drift and performance, manage prompt changes safely in production.

  • Act as a design partner to the GenAI Core platform team, validating platform choices in production.

  • Establish and enforce practical standards for AI feature delivery (evaluation strategy, monitoring, safe prompt/versioning, privacy & safety guardrails).

  • Upskill the team through mentorship, reviews, pairing, and lightweight playbooks.

  • Contribute hands-on in code while blending strategy and execution.

What You Bring

  • Proven experience shipping multiple GenAI features into production at scale, ideally multi-step, tool-using agents.

  • Ability to translate complex business challenges into scalable AI solutions.

  • Deep applied AI judgment: evaluation, retrieval quality, tool use, failure modes.

  • Experience applying evaluation and observability to LLM systems (tests, golden sets, online metrics, monitoring).

  • Strong understanding of privacy and security concerns (prompt injection, handling PII, data leakage).

  • Solid experience with modern AI stack: Vector DBs, orchestration frameworks, LLM APIs.

  • Experience building APIs, services, and data retrieval pipelines (RAG, vector search).

  • Deep proficiency with Python, SQL, and major cloud providers.

  • Background in traditional ML engineering and data systems architecture.

  • Ability to influence cross-functionally with Product/Design/Engineering.

Tech stack context: GCP, BigQuery, Airflow, Python, SQL on the Data side; AWS, Kotlin, Javascript, Typescript on the Product side; containerised with Kubernetes.

Why This Role Fits You

  • You have strong product instincts and build with the user in mind.

  • You’ve moved past prototyping and understand the realities of LLMOps, data retrieval, prompt/context engineering, and model evaluation in production.

  • You don’t just call APIs — you understand the data feeding the AI system and can reason about quality and architecture.

Not a fit if:

  • You want to focus on research/algorithm development.

  • You need a perfectly groomed backlog and structured tooling.

  • You struggle to explain complex AI trade-offs to non-technical stakeholders.

How You’ll Develop

In your first 6 months, 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 GenAI Core tooling and features.

  • Collaborate with Product and Data teams to ship your first feature to production.

Benefits

  • Your own Pleo card (no more out-of-pocket spending!).

  • Lunch covered for workdays (catered or allowance).

  • Comprehensive private healthcare (Vitality, Alan, or MĂ©dis depending on location).

  • 25 days of holiday + public holidays.

  • Hybrid or fully remote working options.

  • Free mental health and well-being support via MyndUp.

  • Paid parental leave.

Interview Process

  1. Intro call (30 min) with Talent Partner.

  2. Technical screening (15–30 min).

  3. System design interview (75 min).

  4. Live coding interview (75 min).

  5. Hiring Manager interview (60 min).

  6. Final leadership interview.

Application tips:

  • Use the “Achieved X, as measured by Y, by doing Z” formula to show impact.

  • Include links to previous companies’ websites.

  • Show evidence of shipping customer-facing AI products at Staff level in SaaS.

Don't let this one get away.

About the company

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