Strong connections with colleagues who support and celebrate you.Fast growth in a company that’s expanding across Europe, the US, and Asia.High energy in a dynamic environment where no two days are the same.Guided freedom to take initiative, solve problems your way, and grow your career.We believe in entrepreneurship, expertise, excellence, and engagement — and we live these values every day. From repairing returned goods to reducing waste, we help brands extend product lifecycles with sustainability in mind.
So if you’re ready to roll out the red carpet for our clients — and for each other — we’ve got a spot for you.
Behind the scenes is where the real excitement begins. Ready to join us?
As an AI Engineer, you build AI solutions end to end: AI workflows and pipelines, intelligent agents and applications with AI integrations, built on Snowflake and Azure AI Foundry. You take a business challenge, design the flow, build it and bring it to production, working closely with business stakeholders, IT and Data Engineers.
Concrete examples of what you will work on: extracting structured data from documents, building the pipelines that process and enrich that data, building agents that answer questions on our own data, and delivering the web applications the business teams use to work with the results.
This is a hands-on building role. Most of the work is applying and integrating existing AI models into workflows and applications. Building and training your own machine learning model is part of the job where that is the better answer, but it is not the centre of gravity.
AI Solution Development (35%)
Design and build AI solutions on prioritized business use cases, from first prototype to productionBuild AI workflows and agents: prompt design, tool calling, retrieval, structured output and guardrailsBuild the AI pipelines behind them: ingestion, processing, enrichment and scheduling, event-driven where that fitsExtract structured data from unstructured sources such as PDF, Excel and e-mail, and make it usable downstreamBuild and deploy the applications around them: APIs, front-end, containers and CI/CDBuild, train and evaluate machine learning models where that is the better fit, for example forecasting or classificationMaintain and improve existing AI solutions in productionUse Case Implementation & Innovation (20%)
Collaborate with business stakeholders to identify and refine AI use cases, for example during AI bootcampsTranslate business questions into a concrete technical design, and make the distinction explicit between a fixed automated flow, a question and answer solution on our own data, and an autonomous agentPerform feasibility assessments and validate potential business valueContribute to shaping and prioritizing the AI roadmapPrototype innovative solutions and experiment with new technologiesData & Platform Integration (15%)
Work in Snowflake as the central platform: Cortex functions, Snowpark, semantic views and container services for hosting applicationsCollaborate with Data Engineers to consume validated data products delivered via SnowflakeDesign and implement data preparation logic for AI use casesLeverage and contribute to the semantic layer so AI solutions use consistent, business-aligned definitionsImplement logging, monitoring and data traceability for AI pipelinesOptimize performance, scalability and cost of AI solutions within Snowflake and AzureAI Data Quality & Grounding (15%)
Ensure high-quality and relevant data is used as input for AI models and agentsDesign context-building mechanisms such as retrieval, context windows and embeddings to ground models in trusted dataImplement guardrails so models only respond based on available data and avoid hallucinationsValidate AI output against known business logic, metrics or datasetsDesign filtering, validation and enrichment logic to reduce noise and inconsistenciesCollaborate with Data Engineers to raise and resolve structural data quality issuesGovernance, Security & Compliance (10%)
Ensure AI solutions comply with internal governance and data protection policiesParticipate in AI risk assessments and documentation processesFollow AI tool registration and approval processesDocument models, assumptions and limitationsApply responsible AI practices: bias awareness, explainability and traceabilityCollaboration & Delivery (5%)
Participate actively in Scrum ceremoniesCollaborate with internal teams: BI, IT and business stakeholdersWork with external partners to co-develop AI solutions and ensure knowledge transfer to internal teamsCommunicate progress, risks and results clearly to stakeholders
