Hariharan Srinivasan

Successfully deploying LLMs on the trading desk hinges on robust data infrastructure—not just model performance. This panel dives into the data engineering required to support LLMs in production, from clean input capture to curated datasets built for the speed and specificity of trading environments. 

• How to capture and process high-frequency trading data for LLM applications
• Best practices for data labeling, storage design, and real-time pipeline optimization
• Meeting governance, auditability, and compliance standards when working with sensitive data; and
• Practical strategies for making LLMs operationally viable in fast-moving, regulated trading settings 

As trading desks expand across equities, fixed income, FX, and crypto, the need for a unified, adaptable tech stack has become mission critical. This panel will explore how firms are integrating OMS, EMS, and data layers to support cross-asset strategies at scale. Panelists will discuss: 

• The challenges of standardizing infrastructure, streamlining workflows, and managing massive data streams while staying agile in a rapidly evolving market; and
• Future-proofing your tech for multi-asset success, whether you're building in-house or partnering with third-party providers