We are seeking an AI/ML Engineer with strong Data Science and Generative AI capabilities to design and deliver next-generation AI solutions across Global Financial Markets.
Designing research and experiment methodology in artificial intelligence, including in generative AI/large language models, and machine learning and reasoning
Data Processing: Assist in building an automated game data collection pipeline to capture precise internal states and construct a high-quality training dataset.
LLM Inference Acceleration. Research and develop advanced compression algorithms to accelerate LLM serving. Focus on KV cache optimization, model quantization, and resolving memory bandwidth bottlenecks during autoregressive decoding.
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 design of end-to-end Enterprise Decision Management (EDM) architectures — connecting data ingestion, predictive model scoring, business rule execution, decision optimisation, and outcome measurement into coherent, production-grade decision services.
Build and maintain automated LLM evaluation pipelines covering multiple dimensions, including general capabilities, Agent capabilities, and persona/role-playing capabilities. Support one-click evaluation, historical result comparison, and regression testing.
Drive Holistic Onboarding: Coordinate and execute the onboarding journey for retail frontline staff and corporate executive-level new hires. You will partner with Operations Management to ensure the "Buddy System" is effectively implemented at the store level for seamless integration.
Build and maintain automated LLM evaluation pipelines covering multiple dimensions, including general capabilities, Agent capabilities, and persona/role-playing capabilities. Support one-click evaluation, historical result comparison, and regression testing.
Reasoning and planning for foundation models: Enhance reasoning and planning throughout the entire development process, including data acquisition, model evaluation, SFT, reward modeling, and reinforcement learning, to improve overall performance.
Large language Model Algorithm Development: Build domain-specific large language models (LLM/MLLM) for e-commerce, integrating domain knowledge to rapidly apply models to business scenarios.
Responsible for the iteration of the underlying architecture of the large model inference engine and end-to-end GPU performance optimization, through means such as operator fusion and compilation optimization, deeply optimizing GPU memory access, computing pipeline, and Stream asynchronous scheduling, eliminating inference computing bottlenecks, improving single-card inference throughput, and reducing inference latency.
Plan and execute assigned learning and development (L&D) programmes to support staff growth. This includes managing budgets, schedules, procurement, communications, administration, and logistics.
Own the agentic-evaluation engine. Build the capability that scores agents on the metrics that matter - intent resolution, tool-selection and parameterisation accuracy, task adherence, planning quality, tool-call ordering, response completeness against a reference, and when an agent should defer to a human.