The individual will be part of the AAIOps team, responsible for shaping a state-of-the-art ecosystem(in terms of tools, technology and datamart) for data, analytics & AI roles within SCMAC while aligning with wider SCB tech simplification effort. The individual will play a crucial part in operationalizing AI/ML models or other analytical pipeline at scale, ensuring efficient and reliable deployment across diverse environments. Responsible for creation, maintenance, and successful execution of a machine learning operation & delivery (MLOps) ecosystem for the continuous delivery of AI artifacts (models, analyses, and predictions) developed by the SCMAC BA CoE, to WRB markets and to WRB digital solutions. Collaboration with both data scientists (SCMAC BA CoE) and software engineers (Enterprise Technology& WRB CIO Data Engineering teams) on the delivery of a fit-for-purpose big data ecosystem for building AI solutions in SCMAC BA CoE, transitioning from proprietary technology (SAS) to open-source technologies. Support the continuous improvement of the tools, technology, and data ecosystem available for all data roles in the SCMAC BA CoE. Implement best practices for version control, testing and CI/CD in ML pipelines. Stay updated on emerging technologies and trends in MLOps, advanced AI (Gen AI) and cloud-based data and analytics platforms (Databricks, Dataiku). Responsible for developing robust data workflows and pipelines using python and pyspark to process and analyze large-scale datasets to adopt new data feeds and feature stores, and in particular - incorporating our WRB unified data model (Athena) to reduce latency and improve the quality of data artifacts.