Team Mission
We are creating a new AI R&D Team in Dublin that will operate as a dedicated innovation capability within IT Corporate Functions, reporting to the Head of Software Development, and working in close collaboration with the IT-CF AI team based in Porto.
The team's mission is to research, design, prototype, and validate AI-driven solutions that enhance business processes and automation across the Euronext Group. The team will focus on emerging AI technologies, intelligent automation, machine learning, generative AI, and integration patterns that delivery teams across the organisation can adopt and industrialise.
The Dublin-based team will:
- Explore and validate new AI technologies and approaches.
- Design future-state AI architecture and integration patterns.
- Develop PoCs and minimum viable products (MVPs).
- Contribute to Euronext's AI strategy and technology roadmap.
- Deliver reusable AI capabilities and accelerators.
- Partner with business stakeholders to identify and assess new AI opportunities.
- Collaborate closely with the Porto AI team to transition successful R&D initiatives into scalable enterprise solutions.
Role Purpose
The Data Engineer for AI is responsible for designing and building the data foundations required to support AI and machine learning initiatives.
The role ensures that data is accessible, reliable, governed, and optimised for experimentation, model training, and future enterprise-scale AI adoption.
Key Responsibilities
- Design and develop data pipelines supporting AI initiatives.
- Prepare, cleanse, transform, and enrich datasets for AI experimentation.
- Build reusable data products that can be leveraged by future AI solutions.
- Support the creation of training datasets and feature engineering activities.
- Implement data governance, security, and monitoring controls.
- Contribute to developing scalable AI data architecture patterns.
- Partner with data owners and business stakeholders across the Euronext Group.
- Work closely with the Porto AI team to establish common data standards and engineering practices.
- Support MLOps and AI operationalisation initiatives.
Required Skills
- Strong experience with Azure Data Services, Microsoft Fabric, Databricks, Synapse, or equivalent technologies.
- Advanced SQL and Python skills.
- Experience building modern data pipelines and data platforms.
- Understanding of AI and Machine Learning lifecycle requirements.
- Experience with cloud-based data architecture.
- Strong analytical mindset and problem-solving skills.
Success Measures
- Availability and quality of datasets supporting AI initiatives.
- Reusability of data products and pipelines.
- Reduction of effort required to onboard new AI use cases.
- Contribution to enterprise AI data architecture standards.