AI Engineer

Ready to Join the Backstage Crew?

At 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.

But 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.

Whether you're on the warehouse floor or behind a screen, you’ll find:

• 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?

Your role

As 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.

You play a key role in delivering innovative AI capabilities while ensuring alignment with governance, data quality standards and business objectives.

Your responsibilities

AI Solution Development (35%)

• Design and develop AI/ML and GenAI solutions based on prioritized business use cases

• Build scalable AI pipelines leveraging Snowflake and integrated tools

• Develop, test, and deploy models (e.g. forecasting, classification, optimization, LLM-based applications)

• Translate prototypes or PoCs into production-ready solutions

• Integrate AI capabilities into BI solutions, workflows, or applications

• Maintain and improve existing AI solutions

Use Case Implementation & Innovation (20%)

• Collaborate with business stakeholders to identify and refine AI use cases (e.g. during AI bootcamps)

• Translate business questions into technical AI solutions

• Perform feasibility assessments and validate potential business value

• Contribute to shaping and prioritizing the AI roadmap

• Prototype innovative AI solutions and experiment with new technologies

Data & Platform Integration (15%)

• Collaborate with Data Engineers to consume and use validated data products delivered via Snowflake

• Design and implement feature engineering and data preparation logic for AI use cases

• Leverage and contribute to the semantic layer (business definitions, metrics, relationships) to ensure AI models use consistent and business-aligned data

• Align AI solutions with existing data models and semantic definitions to avoid duplication or inconsistencies

• Ensure efficient integration of AI models into the data platform and downstream applications

• Optimize performance, scalability, and cost-efficiency of AI solutions within the Snowflake environment

• Implement logging monitoring and data traceability for AI pipelines

AI Data Quality & Grounding (15%)

• Ensure high-quality and relevant data is used as input for AI models and agents

• Design and implement data filtering, validation, and enrichment logic to reduce noise and inconsistencies

• Leverage the semantic layer to ensure consistent business definitions in AI outputs

• Design context-building mechanisms (e.g. retrieval, context windows, embeddings) to ground AI models in trusted data

• Implement guardrails to ensure models only respond based on available data and avoid hallucinations

• Validate AI outputs against known business logic, metrics, or datasets

• Collaborate with Data Engineers to raise and resolve structural data quality issues

Governance, Security & Compliance (10%)

• Ensure AI so