Lead the delivery of end-to-end data engineering engagements, including data architecture, data integration, platform modernization, and cloud migration initiatives.
For large-model training and evaluation scenarios, participate in formulating and continuously iterating data quality standards, judgment rules, and acceptance criteria.
For large-model training and evaluation scenarios, participate in formulating and continuously iterating data quality standards, judgment rules, and acceptance criteria.
For large-model training and evaluation scenarios, participate in formulating and continuously iterating data quality standards, judgment rules, and acceptance criteria.
For large-model training and evaluation scenarios, participate in formulating and continuously iterating data quality standards, judgment rules, and acceptance criteria.
For large-model training and evaluation scenarios, participate in formulating and continuously iterating data quality standards, judgment rules, and acceptance criteria.
For large-model training and evaluation scenarios, participate in formulating and continuously iterating data quality standards, judgment rules, and acceptance criteria.
For large-model training and evaluation scenarios, participate in formulating and continuously iterating data quality standards, judgment rules, and acceptance criteria.
Design, build, and maintain end-to-end machine learning pipelines on Databricks, covering data preparation, feature engineering, model training, evaluation, and deployment.
Our Client is a leading bank in Asia with a global network of around 500 offices in 19 countries and territories in Asia Pacific, Europe and North America. Our client's purpose is to build the future of ASEAN. Client's purpose guides- that defines their unique culture and belief system, anchored on their values of being Honourable, Enterprising, United and Committed, and our people philosophy of Care, Growth and Trust.
Lead the delivery of end-to-end data engineering engagements, including data architecture, data integration, platform modernization, and cloud migration initiatives.
Design, build, and maintain MLOps pipelines for training, testing, deploying, and monitoring AI/ML models in production, ensuring stable, reliable, and scalable performance.
Lead the delivery of end-to-end data engineering engagements, including data architecture, data integration, platform modernization, and cloud migration initiatives.
Own and extend the identity / firmographic and financial layers of the ICP Profile Schema, working from Companies House API data and the Data Platform.
Partner with customers to understand business challenges, operational requirements, and strategic priorities, and identify opportunities where AI/ML, GenAI, and Advanced Analytics can create measurable value.
Own and extend the identity / firmographic and financial layers of the ICP Profile Schema, working from Companies House API data and the Data Platform.