We are looking for an experienced AI/ML Engineer to join our Central Data Platform team and take ownership of the machine learning and GenAI infrastructure that powers advanced analytics and AI use cases across the organisation.
In this role, you will design, build, and maintain end to end ML pipelines in Vertex AI, ensuring that models move seamlessly from prototype to production ready, monitored, cost efficient, and compliant pipelines. You will also pioneer GenAI enablement, including retrieval augmented generation, embeddings, and AI driven applications, while embedding governance, reproducibility, and scalability from day one.
This is a pivotal position with high impact on how AI and machine learning capabilities are delivered, scaled, and governed at Sunday Natural. You will act as the bridge between Data Engineers, Senior Data Analysts, Data Scientists, and business stakeholders, ensuring AI/ML work has measurable, repeatable, and strategic business impact.
What you'll do
Design, build, and maintain modular, reusable ML pipelines in Vertex AI Pipelines covering training, evaluation, deployment, monitoring, and retraining.
Develop GenAI capabilities including embeddings, retrieval pipelines, vector databases, and RAG frameworks for chatbots, personalisation, and semantic search.
Build and manage feature stores and reusable datasets in collaboration with Data Engineers and Analysts.
Productionise workflows with Prefect orchestration and CI/CD pipelines in Bitbucket.
Implement continuous evaluation, drift detection, performance monitoring, rollback strategies, and retraining triggers for deployed models.
Embed GDPR compliance, RBAC, anonymisation, explainability, fairness, and auditability into every model and pipeline.
Document lineage of features, models, and inference workflows, and partner with the Data Governance Lead on ethical AI frameworks.
Translate technical capabilities into business friendly outcomes, communicating trade offs across accuracy, latency, and cost to non technical stakeholders.
Mentor Data Engineers in ML Ops and GenAI techniques, and contribute to internal AI/ML guilds and best practices.