AI Transformation Engineer
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Rydoo is a fast-growing international SaaS company in spend management, helping companies streamline how they capture, control and report employee and company spend.
Our platform offers expense management and accounts payable for thousands of enterprise customers across 130+ countries. We are backed by strong private equity partners and continue to scale rapidly, with a growing footprint in Europe and the US.
We are a team of ~200 people, representing 30+ nationalities. We move fast, work pragmatically and care deeply about building software for modern finance teams.
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 help deliver larger internal projects, taking on more ownership as you grow. 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.
You'll work closely with the AI Transformation Lead, who owns the use-case pipeline and priorities. You'll grow into owning the technical side: how solutions are designed, built, secured and kept running, with guidance from our engineering team on architecture and security.
This role is for someone who wants to learn fast, get real responsibility early and see their work used across a whole company.
What Will You Do
Use case development & delivery
Translate business needs into concrete AI solutionsDesign, build, and deploy internal tools powered by AI (LLMs, automation, workflows)Move quickly from prototype to production-ready solutionsEnsure solutions are reliable, usable, and scalableCross-functional collaboration
Work directly with teams (Sales, Finance, CS, HR, etc.) to understand their workflowsHelp prioritise the most impactful use casesAct as the bridge between what is technically possible and what is operationally useful Enable teams to adopt and use the solutions you buildProduct & technical ownership
Take increasing ownership of internal AI tools, from first prototype to deploymentMake pragmatic trade-offs between speed, quality, and scalabilityContinuously improve solutions based on feedback and usageEnsure a strong focus on user experience and adoptionTooling, automation & integrations
Build automations using tools such as APIs, no-code/low-code platforms, and custom scriptsIntegrate AI capabilities into existing systems (CRM, support tools, finance tools, etc.)Leverage multiple models and providers depending on the use caseEnsure robustness and maintainability of what you buildAI experimentation & innovation
Stay up to date with the latest AI tools and capabilitiesRapidly test new approaches and identify what works in practiceBring new ideas proactively to the organizationGovernance & best practices
Ensure secure and compliant use of data in all solutionsFollow and contribute to internal AI guidelinesBalance experimentation with responsible usageWhat success looks like (12-18 months)
Multiple AI-powered internal tools are in production and used weekly by the teams they were built forClear productivity gains are observed in key workflowsYou can take a use case from idea to a running, monitored solution with little guidanceSolutions are robust, scalable, and well adoptedRequirements
1-3 years of experience in software engineering, data engineering or a similar technical role. Hands-on experience putting LLM-based solutions into production, not only prototyping themYou understand what makes a solution reliable after launch (testing, monitoring, handling failures) Familiarity with SaaS tools such as Salesforce, Slack, M365 or Snowflake is a plusExperience with