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 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.

Your responsibilities

AI Solution Development (35%)

Design and build AI solutions on prioritized business use cases, from first prototype to production
Build AI workflows and agents: prompt design, tool calling, retrieval, structured output and guardrails
Build the AI pipelines behind them: ingestion, processing, enrichment and scheduling, event-driven where that fits
Extract structured data from unstructured sources such as PDF, Excel and e-mail, and make it usable downstream
Build and deploy the applications around them: APIs, front-end, containers and CI/CD
Build, train and evaluate machine learning models where that is the better fit, for example forecasting or classification
Maintain and improve existing AI solutions in production

Use Case Implementation & Innovation (20%)

Collaborate with business stakeholders to identify and refine AI use cases, for example during AI bootcamps
Translate 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 agent
Perform feasibility assessments and validate potential business value
Contribute to shaping and prioritizing the AI roadmap
Prototype innovative solutions and experiment with new technologies

Data & Platform Integration (15%)

Work in Snowflake as the central platform: Cortex functions, Snowpark, semantic views and container services for hosting applications
Collaborate with Data Engineers to consume validated data products delivered via Snowflake
Design and implement data preparation logic for AI use cases
Leverage and contribute to the semantic layer so AI solutions use consistent, business-aligned definitions
Implement logging, monitoring and data traceability for AI pipelines
Optimize performance, scalability and cost of AI solutions within Snowflake and Azure

AI Data Quality & Grounding (15%)

Ensure high-quality and relevant data is used as input for AI models and agents
Design context-building mechanisms such as retrieval, context windows and embeddings to ground models in trusted data
Implement guardrails so models only respond based on available data and avoid hallucinations
Validate AI output against known business logic, metrics or datasets
Design filtering, validation and enrichment logic to reduce noise and inconsistencies
Collaborate with Data Engineers to raise and resolve structural data quality issues

Governance, Security & Compliance (10%)

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 and traceability

Collaboration & Delivery (5%)

Participate actively in Scrum ceremonies
Collaborate with internal teams: BI, IT and business stakeholders
Work with external partners to co-develop AI solutions and ensure knowledge transfer to internal teams
Communicate progress, risks and results clearly to stakeholders

Your profile

What you bring (must-have)

Strong Python, used to build and ship working applications, not only notebooks and experiments
Solid SQL and experience with a cloud data platform. Snowflake is an advantage.
Hands-on experience with LLMs in real solutions: prompt engineering, RAG, embeddings and tool calling or agents
Experience integrating AI services and APIs, for example Azure AI Foundry, OpenAI or Snowflake Cortex
Able to deliver a solution end to end: data flow, API, deployment through Docker and CI/CD, and a usable interface
Machine learning fundamentals, and able to build, train and evaluate a model where that is the better answer than an LLM.
Communicates clearly with business stakeholders and turns their question into a design they recognise

What is a plus (nice to have)

Snowflake Cortex, Snowpark, semantic views and container services
Deeper data science experience: time series forecasting, feature engineering and model evaluation at scale
Front-end experience, for example React
Document processing, OCR or information extraction at scale
MLOps and model lifecycle management
Awareness of AI governance, security and data privacy (GDPR)
Experience in logistics, supply chain or operational environments

What this role is not

Not a research role. Machine learning is part of the work, but most solutions are built by applying and integrating existing models rather than by developing new ones. A profile centred on deep learning research or computer vision does not match this position.
Not an analysis or reporting role. Profiles from BI, data analysis or S&OP analytics without hands-on build experience will not find what they are looking for here.

Experienced or starting

Both are welcome. Experienced candidates who can run a use case independently, and recent graduates with a strong AI profile who want to grow into the role.

How hybrid working is arranged depends on where you sit in that range. Experienced profiles work largely independently and can spend a good part of the week working from home. Starting profiles learn the platform and our way of building by working alongside the team, so they are on site in Grobbendonk for most of the week. That balance shifts towards more home working as you grow into the role.

Soft Skills

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)

What we offer

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).