Develop deep learning AI algorithms for applications with health data. This position involves developing and implementing models like Transformer-based language models, vision Transformer (ViT) image-analysis models, and general neural network based modeling algorithms. In addition to experience in developing such models, there is a need for experience in implementing them effectively in Python. The scientist will develop and implement such models, and will apply them on clinically relevant data connected to the health clusters in Singapore. The goal of the work of this scientist is to use advanced AI technology to improve health outcomes, via precision medicine, population health, and prediction of illness prior to the onset of symptoms (to realize prevention, and enhanced health).
Lead data governance and data management initiatives, translating business requirements into practical technology solutions and architecture blueprints.
The role provides technical authority across AI platform strategy, foundation models, agentic AI, model lifecycle management, AI engineering frameworks and platform capabilities. It works closely with Enterprise Architecture, AI Platform Engineering, AI Platform Operations, Cybersecurity, AI Solution Architects and business stakeholders. Success means continuously evolving an interoperable, reliable and enterprise-ready AI platform while providing technical direction to architects and engineers.
This role sits across platform engineering, ML/LLMOps, IT infrastructure, finance and procurement teams. Success will involve improving cost attribution, utilisation and efficiency across AI workloads while establishing consistent FinOps practices and reporting for projects, teams and applications.
Work within established AI-native engineering workflows, collaborating with AI agents across implementation, testing, debugging, and documentation while validating every output.
Engineering leadership and delivery: Lead a multidisciplinary team of full-stack developers and project managers, including offshore resources, to design, build, operate, and continuously improve AI-powered, web-based, and customer-facing digital applications.
Drive AI adoption: You’ll build AI awareness through training, workshops, and hands-on coaching, and act as an AI implementation consultant to the team while managing the AI budget wisely. By collecting user feedback and driving change management, you’ll make sure solutions are adopted, not just launched.
Shape the business angle of AI product work by assessing user value, operational impact, adoption barriers, stakeholder readiness, productivity gains, and value realisation potential.