Lead the Development of Enterprise AI Agents Design, build, and deploy production-ready AI agents and multi-agent systems that automate complex business processes, augment decision-making, and deliver measurable operational impact across Finance and related business functions.
Develop backend services for AI features: Build and maintain scalable APIs and microservices in Java (Spring Boot) or Python that expose our generative AI capabilities - routing user queries to LLMs and other models, processing data, and returning results securely and efficiently.
Lead the technical architecture and end-to-end development of AI Agentic products for a core enterprise platform, partnering closely with the Product Manager to shape product direction from a technical feasibility standpoint.
Lead the technical architecture and end-to-end development of AI Agentic products for a core enterprise platform, partnering closely with the Product Manager to shape product direction from a technical feasibility standpoint.
Conduct research and development on multimodal processing algorithms and models, including but not limited to image and video understanding and generation, as well as alignment and integration of multimodal information with textual data.
Technical Strategy: Define the AIGC roadmap across pre-training and post-training workstreams — from distributed training infrastructure to alignment, evaluation, and inference deployment.
Technical Strategy: Define the AIGC roadmap across pre-training and post-training workstreams — from distributed training infrastructure to alignment, evaluation, and inference deployment.
Under the guidance of a mentor, participate deeply in the development of core TikTok Search scenarios such as the search results page. Work closely with Product, Design, and Algorithm teams, and drive feature iterations through A/B testing and data analysis to continuously optimize the search experience for hundreds of millions of users.
Technical Strategy: Define the AIGC roadmap across pre-training and post-training workstreams — from distributed training infrastructure to alignment, evaluation, and inference deployment.