Develop and enhance the SeaTalk Open Platform so that internal and external developers can integrate their services and, where relevant, leverage AI capabilities from Sea’s ecosystem.
Delivery & Risk Oversight: Oversee complex, high-stakes AI and data modernization programs — including cloud migrations, RAG implementations, and production-grade AI/ML engineering lifecycles — ensuring on-time, in-budget delivery without business disruption.
Direct the development and execution of AI, analytical and research activities to provide TTSH’s and NHG Health’s management with information and tools for strategic decision making and planning, resource optimization, cost-effective care, and clinical outcomes improvement.
We offer- A fully funded position with an internationally competitive salary- Professional development opportunities, including support for grant applications and participation in conferences and workshops- Access to state-of-the-art research infrastructure, including NSCC’s high-performance computing clusters- A dynamic, interdisciplinary, and collaborative research environment- The position is initially offered for one year, with the possibility of renewalHow to applyWe look forward to receiving your application with the following documents:- Letter of Motivation- CV- Diplomas & TranscriptsWe accept applications submitted via email directed to Prof. Šikić at [email protected].
Your first 12 months: build a research base on customer concerns, purchase motivations, competitors, and approved product claims. Turn those insights into messaging angles, PDP and landing-page copy, and creative briefs with clear hypotheses and supporting evidence. Build reusable AI workflows and review standards, checking outputs for accuracy, substantiation, and brand voice. Work with CRO on copy variants, interpret results together, and apply supported findings to the next round of content. Refine the messaging framework and workflows to improve quality and turnaround while reducing avoidable rework.
Production excellence: Set engineering standards for system design, testing, deployment and observability. Drive improvements in reliability, performance and cost, including the evaluation and productionisation of AI applications.
Optimize foundation model inference performance using techniques such as KV caching, batching strategies, quantization, and optimized runtimes (e.g., vLLM or similar frameworks)
Production excellence: Set engineering standards for system design, testing, deployment and observability. Drive improvements in reliability, performance and cost, including the evaluation and productionisation of AI applications.