Junior AI Engineer
Confirmed on the employer's own hiring board on Nov 12, 2026. First seen by Alion on Nov 12, 2026. Proximus Global scores A on the Alion truth index.
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.