Junior AI Engineer

Turn business questions and data into practical AI solutions. As a Junior AI Engineer, you will combine data analysis, machine learning and modern AI engineering to generate reliable business insights and build scalable applications. You will work with business and domain experts to understand their needs, explore and validate data, communicate clear recommendations, and help translate promising ideas into production-ready solutions.

The role has three complementary pillars: Data Analysis & Business Insights, Machine Learning & Generative AI, and Software & Cloud Engineering. You will receive guidance while progressively taking ownership of analytical, development, testing, deployment and documentation activities.

1. Data Analysis & Business Insights

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.

3. Software & Cloud Engineering

Develop maintainable Python components and contribute to reliable AI applications. 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.

Your profile

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.

Technologies you may work with

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.

What will help you succeed

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 lear