Data / AI Scientist

Proactive Global; Veghel; On-site; EUR 75,000 - 110,000; Full time

Job summary

Proactive Global in Veghel, Netherlands seeks a Data / AI Scientist to design, implement and productise AI capabilities for maintenance and operations in large‑scale warehouses. You will work within a multidisciplinary team to develop AI systems, dashboards and agentic workflows that improve decision‑making and asset performance.

The role focuses on end‑to‑end AI engineering, MLOps, and integration with Azure Databricks, LangGraph, and enterprise data sources.

Qualifications

• 5–7 years of experience implementing AI solutions, preferably in production.

Responsibilities

• Design and develop AI models and algorithms for operational challenges.
• Build and deploy agentic AI applications with multi‑step reasoning and memory orchestration.
• Develop predictive maintenance and ML solutions for warehouse environments.
• Create Generative AI apps leveraging enterprise data and RAG solutions.
• Develop reusable AI services and APIs integrated with cloud platforms.
• Coordinate multi‑agent workflows across models, APIs, and data.
• Move AI prototypes to production with CI/CD, testing, and observability.
• Translate findings into actionable maintenance recommendations.

Skills

Python

PySpark

AI Engineering

LangGraph/LangChain

Azure Databricks

LLM APIs

RAG & Vector search

MLOps

Communication

Team collaboration

Education

Degree in CS/AI/Data Science or related

Tools

Azure OpenAI

Kafka

Git

Job description

Data / AI Scientist | Generative AI & Agentic AI | Temp or Permanent | Veghel, Netherlands

The Role

You'll join a multidisciplinary product team within the Digital Lifecycle Performance department, working as part of the Maintenance Assistance team.

You'll play a key role in developing and productising AI capabilities that improve operational performance, maintenance decision‑making and the wider customer lifecycle across large-scale automated warehouse environments.

Your responsibilities will include:

• Designing and developing AI models and algorithms to solve real‑world operational challenges.
• Building and implementing agentic AI applications incorporating multi-step reasoning, tool use, memory orchestration and human-in-the-loop controls.
• Developing advanced predictive maintenance and machine learning solutions.
• Building Generative AI applications that leverage enterprise and operational data.
• Developing Retrieval-Augmented Generation (RAG) solutions using structured data, documents, events and knowledge graphs.
• Building reusable AI services and APIs that integrate with existing digital products and cloud services.
• Developing multi-agent workflows that coordinate models, APIs, operational data and business logic.
• Taking AI prototypes through evaluation, productisation and deployment into production environments.
• Implementing automated testing, CI/CD, observability and end-to-end MLOps pipelines.
• Analysing operational and asset data, translating findings into actionable recommendations for maintenance and operations teams.
• Working closely with software engineers, data engineers and domain specialists to integrate AI solutions into existing systems.
• Exploring emerging developments in Generative AI, Machine Learning, process mining and predictive analytics.

The Technical Environment

This position requires a strong combination of Data Science knowledge and practical AI Engineering experience.

Key technologies and areas of expertise include:

• Programming: Python, PySpark, Pandas and Scikit-learn.
• Cloud & Data: Microsoft Azure, Azure Databricks and Azure AI services.
• Generative AI: LLMs, Azure OpenAI, prompting and structured outputs.
• Agentic AI: LangGraph, LangChain, Semantic Kernel, AutoGen or comparable frameworks.
• RAG & Knowledge Grounding: Embeddings, vector search, hybrid search and knowledge graphs.
• Machine Learning: Predictive modelling, anomaly detection and predictive maintenance.
• MLOps: End-to-end machine learning pipelines, model deployment, CI/CD and automated testing.
• Software Engineering: API development, Git, scalable application design and observability.
• Data Engineering: Operational data, event-driven architectures, Kafka and time-series data.

What We're Looking For

• 5–7 years of experience implementing AI solutions and applications, ideally within production environments.
• Strong foundations in Data Science, Machine Learning and AI Engineering.
• Proven hands‑on experience developing and deploying Generative AI or Agentic AI applications.
• Strong Python and PySpark development skills.
• Practical experience with Microsoft Azure, particularly Azure Databricks.
• Experience with LLM APIs and platforms such as Azure OpenAI.
• Strong understanding of RAG, embeddings, vector search and knowledge‑grounding techniques.
• Experience with agent orchestration frameworks such as LangGraph, LangChain, Semantic Kernel or AutoGen.
• Demonstrable MLOps experience, including end‑to‑end data and machine learning pipelines.
• Experience taking prototypes and transforming them into scalable production applications.
• Ability to work with multidisciplinary engineering teams, including software, data and maintenance specialists.
• Strong communication skills and the ability to translate technical findings into practical business solutions.
• Fluent English. Dutch would be advantageous.
• A degree in Computer Science, Artificial Intelligence, Data Science or a related discipline, or equivalent professional experience.

Personality and Team Fit

This is particularly important for this position.

The organisation is looking for someone who:

• Is naturally collaborative and enjoys working within multidisciplinary engineering teams.
• Can work effectively with both back‑end and front‑end engineers, Data Scientists and domain specialists.
• Takes ownership of solutions beyond initial development and experimentation.
• Is comfortable working in exploratory environments where requirements, problems and data availability can evolve.
• Can challenge existing approaches and identify opportunities to improve processes.
• Communicates technical concepts clearly to both technical and non-technical stakeholders.
• Has a practical mindset and is motivated by delivering solutions that make a measurable difference.
• Understands the importance of responsible AI, security, data privacy and human oversight.