Efficiency: Designing and implementing software platforms and monitoring frameworks for efficient, automated, and intelligent service-oriented architecture (SOA) governance.
AI scenarios: Participate in the application of AI in client-side development and core business scenarios, helping build an AI-driven R&D and system capability framework. This includes, but is not limited to, improving development efficiency through AI, such as code generation, workflow-driven development, and business understanding; participating in the design and practice of AI Native / Agentic Commerce capabilities, including interaction models and capability abstractions for agents; exploring and contributing to cutting-edge AI payment capabilities and industry-standard protocols; and promoting intelligent capabilities across payment and financial flows, such as decision intelligence, recommendation intelligence, and experience intelligence, to continuously improve engineering efficiency and business outcomes.
Security Engineering & AI – Explore and develop AI-powered solutions for security engineering, including vulnerability discovery, security analysis, code review, and security automation.
Problem Solving for Governance Applications: Address challenges such as long text/sequence modeling, few-shot learning, content moderation, violation detection, and policy recommendation using large models and multimodal approaches.
Strong coding skills in at least one of the following programming frameworks, e.g. Python, Java, C/C++, Spark etc.; Familiar with at least one of the following deep learning frameworks, e.g., PyTorch, TensorFlow, Keras etc.
Collaborate with cross-functional teams to deliver event data ingestion, transformation, and storage solutions that meet diverse business requirements.
Problem Solving for Governance Applications: Address challenges such as long text/sequence modeling, few-shot learning, content moderation, violation detection, and policy recommendation using large models and multimodal approaches.
Responsible for the development of deep learning and operations research models and related intelligent systems for the supply chain and logistics of the global E-Commerce business.
Collaborate with cross-functional teams to deliver event data ingestion, transformation, and storage solutions that meet diverse business requirements.
AI scenarios: Participate in the application of AI in client-side development and core business scenarios, helping build an AI-driven R&D and system capability framework. This includes, but is not limited to, improving development efficiency through AI, such as code generation, workflow-driven development, and business understanding; participating in the design and practice of AI Native / Agentic Commerce capabilities, including interaction models and capability abstractions for agents; exploring and contributing to cutting-edge AI payment capabilities and industry-standard protocols; and promoting intelligent capabilities across payment and financial flows, such as decision intelligence, recommendation intelligence, and experience intelligence, to continuously improve engineering efficiency and business outcomes.