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# AI Engineer

- **Company:** Möbius Business Redesign
- **Location:** Gent, Belgium
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, Python (Programming Language), Machine Learning, Tensorflow, Systems Integration, Pytorch, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Generative AI, Scikit Learn, Information Technology, Machine Learning Operations
- **Published:** October 5, 2026
- **Apply:** https://www.mobius.eu/en/jobs/ai-engineer

## About the Role

Note: This vacancy is meant for candidates who are fluent in Dutch, besides having the right technical background., * Holds a master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering or a related field.

 * Can demonstrate a few years of relevant work experience.
 * Has practical experience with LLMs and GenAI tools and techniques.
 * Has extensive deployment experience with cloud platforms such as Microsoft Azure, reliably bringing AI models into production and maintaining them.
 * Is proficient in Python and has experience building and maintaining APIs.
 * Has a strong sense of business context and naturally brings it into technical decision-making.
 * Communicates clearly in Dutch and English, for both technical and non-technical audiences. Knowledge of French is an asset.
 * Is proactive, curious and a team player who enjoys working with various stakeholders.

## Description

* Working together with clients and internal teams to properly understand their needs and processes, and to (help) translate these into concrete AI and ML solutions.
 * Designing and developing AI applications, with a strong focus on Large Language Models (LLMs) and Generative AI. You apply techniques such as prompt engineering and RAG to build reliable, pragmatic and high-performing solutions that meet specific client needs and use cases.
 * Developing solutions using Python and relevant AI/ML frameworks (e.g. Scikit-learn, TensorFlow and PyTorch), and integrating AI functionality into broader applications and processes, including via APIs.
 * Designing technical architectures for AI/ML solutions, making deliberate choices around modular components, integrations, scalability, reliability, security, manageability and cost efficiency. You consider not only what works technically, but also what is appropriate and sustainable for the specific use case.
 * Building robust and scalable AI/ML pipelines based on that architecture and subsequently bringing them into production: from proof of concept to reliable deployment in cloud environments such as Azure - in short, MLOps from A to Z.
 * Combining GenAI with classical Machine Learning techniques where relevant (e.g. classification, regression, clustering or time series analysis). You choose the technique that best fits the problem, rather than treating AI or LLMs as a goal in themselves.