AI Engineer
Bleckmann · Waregem Kalkhoevestraat 12 ·
- Work mode
- Hybrid
- Category
- AI engineering
Bleckmann · Waregem Kalkhoevestraat 12 ·
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.
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
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
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
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
Ensure AI solutions comply with internal governance and data protection policies
Participate in AI risk assessments and documentation processes
Follow AI tool registration and approval processes
Document models, assumptions, and limitations
Apply responsible AI practices (bias awareness, explainability, traceability)
Participate actively in Scrum ceremonies (daily stand-ups, sprint planning, retrospectives)
Collaborate with internal teams (BI, IT, Business stakeholders)
Work with external partners to co-develop AI solutions
Ensure knowledge transfer from partners to internal teams
Support deployment, monitoring, and continuous improvement of solutions
Communicate progress, risks, and results clearly to stakeholders
Your profile
Solid experience with SQL and working in Snowflake (or similar cloud data platforms)
Understanding of data modeling concepts and ability to work with structured data environments
Experience with machine learning techniques (regression, classification, clustering, etc.)
Experience with Generative AI / LLMs and agent-based solutions
Prompt engineering
Retrieval-Augmented Generation (RAG)
Embeddings and vector search
Experience integrating AI services or APIs (e.g. OpenAI, Azure AI, etc.)
Understanding of AI limitations, including hallucination risks and mitigation techniques
Experience working with data pipelines and data products (as consumer, not owner)
Ability to design feature engineering and data preparation logic
Understanding of semantic layer concepts and business metrics definitions
Awareness of performance optimization and cost control in cloud environments
Strong analytical and problem-solving mindset
Ability to translate business problems into technical AI solutions
Focus on delivering value, not just building models
High attention to data quality, reliability, and correctness
Passion for innovation and continuous learning in AI
Strong collaboration skills in cross-functional teams
Ability to work effectively in an Agile / Scrum environment
Comfortable working in a hybrid setup (internal + external partners)
Proactive and ownership-driven mindset
Ability to manage ambiguity and evolving requirements
Ability to explain complex AI concepts in a clear and business-friendly way
Strong communication towards: Business stakeholders (translate needs into solutions) and Technical teams (align with Data Engineers / IT)
Document AI solutions and decisions clearly
Present results and insights in an understandable way
Comfortable challenging requirements when needed (critical thinking)
Experience with Snowflake AI capabilities (e.g. Cortex, Snowpark,…)
MLOps / model lifecycle management
Experience working in an Agile/Scrum environment (Scrum methodology can be learned)
Ability to collaborate with external partners / vendors
Awareness of AI governance, security, and data privacy (GDPR)
Experience in Logistics / supply chain / operational environments
Building AI agents in enterprise environments
Working in governed data environments (enterprise BI setups)
A role with direct impact on business development
Exposure to international clients, carriers and internal stakeholders
A dynamic environment where requests are varied and often cross-functional
Room to improve processes, templates, data quality and ways of working
Guided freedom to take initiative and grow your expertise
A collaborative team environment with short communication lines
Hybrid working possibilities, depending on location and business needs
At Bleckmann, we are guided by our values: We take a parachute and jump (Entrepreneurship), we unpack our knowledge (Expertise), we raise the bar with every box (Excellence), and we spark energy that connects (Engagement).