Internal AI Tools Engineer

We are Rydoo An AI-driven B2B SaaS company that simplifies how businesses manage spend, from employee expenses to supplier invoices.

Our two business lines — expense management and accounts payable — help finance teams work more efficiently and stay in control of company spending. Our expense management platform makes submitting, approving, and reimbursing expenses easy, while our accounts payable solution uses AI to automate invoice processing and approval workflows.

With a product-first mindset, a passion for great UX, and a commitment to innovation, we’re empowering thousands of companies, and their people, to work smarter every day.

Rydoo in a snapshot

• Ambitious international team (250+ and growing)
• 40+ nationalities speaking over 15 languages
• 12 offices across 3 continents
• Easy-to-use spend management app available on iPhone and Android
• Supporting over 10,000+ clients and 1 million+ users worldwide
• 4.5 employer score onGlassdoor

Role Purpose

At Rydoo, AI is a key lever to improve how we operate internally: faster execution, better decisions, and reduced manual work.

To unlock this potential, we are creating a dedicated role focused on building and deploying AI-powered solutions for internal teams. You will connect data across our internal systems, create the shared building blocks that AI solutions sit on, and deliver the larger internal projects end to end. Alongside that, you will ship focused use cases with individual teams (Sales, Finance, Customer Success, HR, Product).

The Role

You are a builder, one who cares about what happens after the demo.

Your mission is to turn AI opportunities into deployed, reliable solutions that teams use every day, and to make each one cheaper and faster to build than the last by investing in shared data access, integrations and tooling.

You will not work on Rydoo’s customer-facing product, but on internal tools, automations, and workflows that increase efficiency and quality across the company.

You will collaborate closely with the AI Transformation Lead and business teams to identify needs, prioritise use cases, and deliver solutions end-to-end.

What Will You Do

Use case development & delivery

• Translate business needs into concrete AI solutions
• Design, build, and deploy internal tools powered by AI (LLMs, automation, workflows)
• Move quickly from prototype to production-ready solutions
• Ensure solutions are reliable, usable, and scalable

Cross-functional collaboration

• Work directly with teams (Sales, Finance, CS, HR, etc.) to understand their workflows
• Help prioritise the most impactful use cases
• Act as the bridge between what is technically possible and what is operationally useful
• Enable teams to adopt and use the solutions you build

Product & technical ownership

• Take ownership of internal AI products from ideation to deployment
• Make pragmatic trade-offs between speed, quality, and scalability
• Continuously improve solutions based on feedback and usage
• Ensure a strong focus on user experience and adoption

Tooling, automation & integrations

• Build automations using tools such as APIs, no-code/low-code platforms, and custom scripts
• Integrate AI capabilities into existing systems (CRM, support tools, finance tools, etc.)
• Leverage multiple models and providers depending on the use case
• Ensure robustness and maintainability of what you build

AI experimentation & innovation

• Stay up to date with the latest AI tools and capabilities
• Rapidly test new approaches and identify what works in practice
• Bring new ideas proactively to the organization

Governance & best practices

• Ensure secure and compliant use of data in all solutions
• Follow and contribute to internal AI guidelines
• Balance experimentation with responsible usage

What success looks like (12-18 months)

• Multiple AI-powered internal tools are in production and used weekly by the teams they were built for
• Clear productivity gains are observed in key workflows
• A pipeline of new AI use cases is continuously identified and executed
• Solutions are robust, scalable, and well adopted

• 3-8 years of experience in software engineering, product engineering, or similar roles
• Hands-on experience putting LLM-based solutions into production, not only prototyping them
• Experience making a solution reliable after launch: evaluation, testing, monitoring, handling failure cases