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# AI Engineer

- **Company:** Proximus
- **Location:** Belgium
- **Experience:** Starter
- **Contract:** Internship / Graduate position
- **Skills:** LangGraph Framework, Artificial Intelligence, Data Analysis, Microsoft Azure, Continuous Integration, Data Validation, Data Visualization, DevOps, Github, Graph Database, Python (Programming Language), Machine Learning, Standard Sql, SQL Databases, Enterprise Search, Cloud Platform System, Retrieval-Augmented Generation, Large Language Models, Llamaindex, Generative AI, Agentic-AI, Git, Pandas, Scikit Learn, Information Technology, CrewAI, AutoGen, Machine Learning Operations, GPT, Databricks
- **Published:** November 12, 2026
- **Apply:** https://proximus.csod.com/ux/ats/careersite/10/home/requisition/20178?c=proximus

## About the Role

* Master's degree in Artificial Intelligence, Computer Science, Engineering, Data Science or a related technical field, or equivalent practical experience.
 * Up to 2 years of relevant professional experience. Internships, academic projects and personal AI or data projects are valued.
 * Good foundation in Python and common data-analysis or machine-learning libraries such as pandas and scikit-learn.
 * Working knowledge of SQL and experience analysing structured datasets.
 * Understanding of statistics, data validation, machine learning and effective data visualization.
 * Strong interest in Generative AI, LLMs, RAG and modern AI architectures.
 * Ability to connect analytical findings to business questions and communicate recommendations to technical and non-technical audiences.
 * Fluent in English. French and/or Dutch is a strong asset., * Python, SQL, pandas, scikit-learn and data-visualization libraries.
 * LLM frameworks such as LangChain or LlamaIndex, and agentic frameworks such as LangGraph, AutoGen or CrewAI.
 * Microsoft Azure, Azure AI Search, vector databases, graph databases and knowledge graphs.
 * Databricks, including catalogue structures and AI/ML capabilities.
 * Git, GitHub, Azure DevOps or GitHub Actions, CI/CD pipelines and MLflow., You do not need to master every technology listed above. We are looking for strong analytical and technical fundamentals, hands-on curiosity and the ability to learn and grow in a supportive team environment.

## Description

* Use SQL and Python to explore, combine, clean, filter and validate data and answer business questions in a reliable, repeatable way.
 * Perform exploratory data analysis to identify patterns, trends, correlations, anomalies and data-quality issues.
 * Translate business questions into structured analytical approaches, hypotheses and measurable outcomes.
 * Apply statistical methods and effective visualizations to validate findings and communicate them clearly.
 * Independently investigate unfamiliar datasets, ask the right questions and select an appropriate analytical or machine-learning approach.
 * Explain evidence, model drivers, reliability, assumptions, limitations and potential bias to technical and non-technical stakeholders.
 * Produce concise analytical notes that document conclusions, limitations and actionable recommendations.
 * Work closely with domain experts to gather context, respectfully challenge assumptions, validate conclusions and document decisions.

 2. Machine Learning & Generative AI

 * Build, test and explain practical machine-learning solutions, including regression, classification, clustering and similarity analysis.
 * Use anomaly-detection and forecasting techniques to identify unusual patterns and estimate future outcomes.
 * Contribute to Generative AI and LLM-based applications, including prompting, Retrieval-Augmented Generation solutions and basic agentic workflows.
 * Assess the quality and suitability of AI outputs using appropriate evaluation methods and business criteria.
 * Prototype and validate AI solutions with business stakeholders before further industrialization., * Contribute to deploying, monitoring and maintaining AI solutions in cloud environments, preferably Microsoft Azure.
 * Apply software-engineering, DevOps and MLOps practices, including Git, testing, CI/CD and reproducible experimentation.
 * Collaborate with AI engineers, data scientists, data engineers and business experts throughout the solution lifecycle.
 * Create clear technical documentation and contribute to team knowledge sharing and continuous improvement., * Business curiosity: you seek to understand the underlying business question before selecting a technical approach.
 * Critical thinking: you check assumptions, explore alternative explanations and validate conclusions against evidence.
 * Problem solving: you can structure ambiguous problems and learn unfamiliar technologies or datasets quickly.
 * Collaboration: you work constructively with engineering, data and business colleagues.
 * Communication: you document your work clearly and adapt explanations to the audience.
 * Quality and ownership: you test your work, pay attention to detail, ask for support when needed and follow through on commitments.