Apply AI/ML expertise to build scalable solutions in areas such as Generative AI, personalization, image and speech recognition, NLP, recommendation systems, and time series forecasting.
Data analysis and insights: Perform exploratory data analysis, generate insights, and present findings to stakeholders. Use statistical methods and visualization techniques to communicate complex concepts and patterns effectively.
Conduct reinforcement learning (RL) training experiments for long-horizon tasks, including hyperparameter tuning, performance analysis, and iterative optimization.
Build and maintain an automated LLM evaluation pipeline covering multiple dimensions, including general capabilities, Agent capabilities, and persona/role-playing. The pipeline should support one-click evaluation, historical result comparison, and regression testing.
Data Center & Deployment and Project Management: Coordinate data center readiness, including power, cooling, networking, and rack capacity, and oversee GPU server installation and cluster deployment across China and overseas markets.
Conduct reinforcement learning (RL) training experiments for long-horizon tasks, including hyperparameter tuning, performance analysis, and iterative optimization.
Build and maintain an automated LLM evaluation pipeline covering multiple dimensions, including general capabilities, Agent capabilities, and persona/role-playing. The pipeline should support one-click evaluation, historical result comparison, and regression testing.
Data Center & Deployment and Project Management: Coordinate data center readiness, including power, cooling, networking, and rack capacity, and oversee GPU server installation and cluster deployment across China and overseas markets.
Represent the business as a Martech specialist and partner with IT, vendors, Analytics and business teams to design, enhance and implement Martech/AdTech capabilities that support data-driven and personalised customer engagement.
Lead technical solutioning in client pre-sales and discovery across all sectors — translating business problems into AI architectures (RAG pipelines, agentic workflows, SDLC automation, data platforms, model risk frameworks)
Conversation & user understanding — Research how AI products communicate with users at depth, and help build a long-term, compounding model of individual user context and preference.
Lead AI & Data Science Initiatives: Drive large-scale AI/ML programmes, define strategic roadmaps, and translate business priorities into measurable outcomes, leveraging Generative AI and LLMs where relevant.
Lead AI & Data Science Initiatives: Drive large-scale AI/ML programmes, define strategic roadmaps, and translate business priorities into measurable outcomes, leveraging Generative AI and LLMs where relevant.
Evaluate AI-generated pharmaceutical content: Assess scientific, technical, and regulatory responses for accuracy, completeness, consistency, and compliance with industry standards.