AI Engineer

AI overview

Analyze, develop and deliver AI solutions, providing user training and support, while working with advanced data modeling and cloud technologies.
  • Analysis, development, testing and delivery in production of proof of concepts, pilots and projects for a variety of end-users using Artificial Intelligence (AI).
  • Provide advice, support and training for AI related activities for technical and non-technical users (Modelling, Design, Architecture Review, AI projects, Standards and processes, Centre of Competence)
  • Prepare analysis and architecture deliverables (eg: building blocks) concerning AI to contribute to Reference Architecture and AI Data Platform

More than 4 years of experience in the following areas is required:

  • Prompt engineering or related fields:
    • Develop and refine prompts for AI models to achieve desired outcomes
    • Test, evaluate, and iterate on prompt strategies.
    • Analyse and report on model behaviour based on different prompt designs.
  • Ability to effectively communicate with business stakeholders and explain complex technical elements in a simple way.
  • Technical guidance and troubleshooting including solution finding for highly complicated issues.
  • Programming languages for data analytics and artificial intelligence (eg: python)
  • Industrialization and sizing of AI solutions using private cloud environments, containerisation (eg: Kubernetes, ArgoCD) and CI/CD techniques (eg: Bamboo, Git, ...).
  • MLOps tools, testing and quality assurance.
  • Developing with reusable open-source large language models (eg: Gemma 3) and Generative AI APIs (eg: openai)

Additionally, experience in the following areas will be an asset:

  • Analysis and validation of business cases and/or maturity assessment studies.
  • Analysis and validation of technological orientations and ability to cope with fast changing technologies.
  • Analytical thinking, by using logic and reasoning to identify patterns and gaps while analysing and exploring data to reach data-driven decision-making.
  • Data modelling for data lakes and data warehouses (eg: Medallion Architecture, Star Schema and Kimball)
  • SQL (eg: Oracle, SQL Server,…)
  • ETL/ELT (eg: Kestra/Airflow orchestration, DBT transformations)
  • Data visualisation and dashboarding tools (eg: PowerBI, Superset)

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