Run the template against multiple countries in sequence, adapting for language, data source variability and regulatory differences while keeping the core architecture unchanged.
Champion rapid experimentation, prototyping and iterative development, using modern AI-assisted and vibe-coding approaches where appropriate to shorten development cycles
Establish architectural best practices for AI model deployment, evaluation, versioning, monitoring, retraining, performance optimisation and retirement. Drive the adoption of enterprise AI patterns, including RAG, agentic AI, workflow orchestration and API integrations.
The Data Engineer designs, builds and maintains data pipelines, integrations and data structures that support AI, machine learning, analytics and automation solutions. The role works with approved enterprise data platforms, including Databricks and Microsoft Fabric (medallion architecture), to ensure data is reliable, secure, well-governed and ready for approved technical and business use cases.
Work closely with stakeholders to refine dashboard design and usability, ensuring visualisations are intuitive, relevant and aligned with business expectations.
Supporting AI experimentation: Contribute to model selection, training, experimentation, evaluation, testing and validation, with exposure to deployment through batch jobs, services and APIs.