Must-haves

A bachelor's or master's degree in computer science, artificial intelligence, data science or a related field, or equivalent professional experience.
Strong Python and PySpark skills, with solid software engineering: API development, automated testing, Git, pandas, scikit-learn and TensorFlow.
Proven experience with Azure, especially Databricks, for ML model development and MLOps (end-to-end data and ML pipelines).
Hands-on experience building generative or agentic AI applications, using frameworks such as LangGraph, LangChain, Semantic Kernel, Pydantic AI or AutoGen.
Experience with LLM APIs and platforms such as Azure OpenAI, and with prompting, structured outputs, RAG, embeddings and vector or hybrid search.
Experience designing autonomous workflows, decision logic and safe tool execution, and evaluating AI-generated output.
A security-first mindset: privacy, authorisation, prompt injection, data leakage and responsible AI.
Familiarity with working according to CRISP-DM.
Comfortable in exploratory environments where problems and data evolve; you structure and validate new use cases.
A strong communicator who explains technical results to non-technical stakeholders, with experience working with colleagues around the world.
Fluent English.

Nice-to-haves

Experience developing predictive maintenance models.
Knowledge of event-driven architecture, Kafka, knowledge graphs, or time-series and operational data.
Experience with Azure AI services, Databricks AI services or Kubernetes.
Experience evaluating AI systems and designing human oversight and feedback mechanisms.
Familiarity with industrial automation in warehouse operations.
Dutch.

Curious about new AI technology but pragmatic about turning it into dependable products? Then we'd like to hear from you.

You build agentic assistants that analyse operational situations, retrieve relevant information and recommend actions.
You design multi-agent and tool-using workflows that combine data, APIs, models and business rules, with multi-step reasoning, memory and human-in-the-loop controls.
You develop retrieval-augmented generation (RAG) solutions grounded in structured data, documents, events and knowledge graphs.
You analyse operational and asset data and develop and validate predictive and decision-support models, for example for maintenance, anomaly investigation and root-cause analysis.
You apply AI to process mining and explore new use cases and asset types: you assess data availability and quality, define the approach and validate the value.
You build reusable AI services and APIs that integrate with operational data, digital products and existing cloud services.
You productise prototypes with solid engineering practices (automated testing, CI/CD, MLOps, observability) and support deployment to production.
You translate results into actionable insights and recommendations, together with domain experts and end users.
You follow developments in generative AI, machine learning, process mining and event knowledge graphs, and turn promising research into practical capabilities.

Medior Data Scientist / AI Engineer

Taking on tomorrow