Demonstrable payment processing and clearing knowledge especially data mapping and modelling to support triaging issues raised in development and production
Work closely with the SPEED architect, quantitative researchers, and the business to provide high ROI solutions that are aligned with both the business and the platform strategy.
Partner closely with IC design teams to ensure to deliver comprehensive technical specifications, rigorous benchmark reports, and high-value patent proposals.
Make integration effortless: Create reusable frameworks, SDKs, and configuration patterns so new connectors and integrations can be built quickly and maintained reliably.
Infrastructure & Performance Optimization: Manage and optimize GPU/accelerator infrastructure (on cloud platforms like AWS, Azure, GCP or on-prem NVIDIA clusters) to support high-performance model training and inference. Drive token efficiency, latency reduction, and cost optimization in AI pipelines (through techniques like model quantization, batching, and caching).
Technical Leadership & Solution Ownership: Lead the technical direction of AI initiatives by driving architecture decisions, establishing best practices, mentoring team members, and guiding the end-to-end delivery of scalable AI solutions.
Kubernetes Service Platform Development of managed Kubernetes services with GPU-native orchestration, intelligent scheduling, distributed workload management, cluster lifecycle management, observability, resource isolation, and support for large-scale AI training and inference workloads
Kubernetes Service Platform Development of managed Kubernetes services with GPU-native orchestration, intelligent scheduling, distributed workload management, cluster lifecycle management, observability, resource isolation, and support for large-scale AI training and inference workloads
Based on Southeast Asia business scenarios and Agent applications, participate in designing AI business implementation scenarios to help improve business efficiency and solve business pain points.
Integrate AI Agents into daily R&D workflows and explore their applications in scenarios such as coding assistance, intelligent debugging, automated testing, issue troubleshooting, and business process automation.
Assist in advancing program optimizations such as performance monitoring, exception management, and cache preloading. Participate in the full lifecycle from data collection to solution implementation, and develop a deep understanding of the engineering systems behind large-scale applications.