AI Engineer

ph3bReady to Join the Backstage Crew? /b /h3 pAt Bleckmann, we’ve been delivering on promises since 1862. As a market leader in supply chain management for fashion and lifestyle brands, we keep the show running behind the scenes — from moving boxes to moving data, from pack ship to IT and HR. /p pBut we’re not just logistics experts. We’re The Backstage Crew — a tight-knit team of 6,500+ people who make fashion and lifestyle brands shine by doing the work that matters most, out of the spotlight but never out of impact. /p pWhether you're on the warehouse floor or behind a screen, you’ll find: /p ul lipbStrong connections /b with colleagues who support and celebrate you. /p /li lipbFast growth /b in a company that’s expanding across Europe, the US, and Asia. /p /li lipbHigh energy /b in a dynamic environment where no two days are the same. /p /li lipbGuided freedom /b to take initiative, solve problems your way, and grow your career. /p /li /ul pWe believe in bentrepreneurship, expertise, excellence, /bandb engagement /b — and we live these values every day. From repairing returned goods to reducing waste, we help brands extend product lifecycles with sustainability in mind. /p pSo if you’re ready to roll out the red carpet for our clients — and for each other — we’ve got a spot for you. /p pBehind the scenes is where the real excitement begins. Ready to join us? /p h3bYour role /b /h3 pAs an AI Developer, you contribute to the development, implementation and continuous improvement of AI-driven solutions, intelligent agents and machine learning applications. You translate business challenges into scalable AI solutions while working closely with business stakeholders, IT teams, Data Engineers and external partners. /p pYou play a key role in delivering innovative AI capabilities while ensuring alignment with governance, data quality standards and business objectives.br/ /p h3bYour responsibilities /b /h3 h3bAI Solution Development (35%) /b /h3 ul lipDesign and develop AI/ML and GenAI solutions based on prioritized business use cases /p /li lipBuild scalable AI pipelines leveraging Snowflake and integrated tools /p /li lipDevelop, test, and deploy models (e.g. forecasting, classification, optimization, LLM-based applications) /p /li lipTranslate prototypes or PoCs into production-ready solutions /p /li lipIntegrate AI capabilities into BI solutions, workflows, or applications /p /li lipMaintain and improve existing AI solutions /p /li /ul h3bUse Case Implementation Innovation (20%) /b /h3 ul lipCollaborate with business stakeholders to identify and refine AI use cases (e.g. during AI bootcamps) /p /li lipTranslate business questions into technical AI solutions /p /li lipPerform feasibility assessments and validate potential business value /p /li lipContribute to shaping and prioritizing the AI roadmap /p /li lipPrototype innovative AI solutions and experiment with new technologies /p /li /ul h3bData Platform Integration (15%) /b /h3 ul lipCollaborate with Data Engineers to consume and use validated data products delivered via Snowflake /p /li lipDesign and implement feature engineering and data preparation logic for AI use cases /p /li lipLeverage and contribute to the semantic layer (business definitions, metrics, relationships) to ensure AI models use consistent and business-aligned data /p /li lipAlign AI solutions with existing data models and semantic definitions to avoid duplication or inconsistencies /p /li lipEnsure efficient integration of AI models into the data platform and downstream applications /p /li lipOptimize performance, scalability, and cost-efficiency of AI solutions within the Snowflake environment /p /li lipImplement logging monitoring and data traceability for AI pipelines /p /li /ul h3bAI Data Quality Grounding (15%) /b /h3 ul lipEnsure high-quality and relevant data is used as input for AI models and agents /p /li lipDesign and implement data filtering, validation, and enrichment logic to reduce noise and inconsistencies /p /li lipLeverage the semantic layer to ensure consistent business definitions in AI outputs /p /li lipDesign context-building mechanisms (e.g. retrieval, context windows, embeddings) to ground AI models in trusted data /p /li lipImplement guardrails to ensure models only respond based on available data and avoid hallucinations /p /li lipValidate AI outputs against known business logic, metrics, or datasets /p /li lipCollaborate with Data Engineers to raise and resolve structural data quality issues /p /li /ul h3bGovernance, Security Compliance (10%) /b /h3 ul lipEnsure AI solutions comply with internal governance and data protection policies /p /li lipParticipate in AI risk assessments and documentation processes /p /li lipFollow AI tool registration and approval processes /p /li lipDocument models, assumptions, and limitations /p /li lipApply responsible AI practices (bias awareness, explainability, traceability) /p /li /ul h3bCollaboration Delivery (5