AI Application Development:
Design and develop LLM-based services, delivering intelligent Q&A, SDLC, and agent capabilities for trading systems
Build AI Agent workflows with multi-turn conversation, tool-calling, and autonomous decision-making, applied to financial business development scenarios
Build and optimize Retrieval-Augmented Generation (RAG) pipelines, structuring financial domain knowledge — business rule documents, interface specifications, historical BAU patterns — into a queryable knowledge base
Design vector retrieval strategies, query rewriting, and re-ranking algorithms with continuous iteration
Develop MCP Servers exposing code search, database query, and document retrieval tools to agents, integrated with internal enterprise systems
Optimize prompt engineering and build Eval frameworks to quantify agent accuracy and execution quality in financial business scenarios
Performance & Engineering:
Monitor inference latency, token cost, and system stability to ensure high availability in production
Track LLM and Agent frontier developments, exploring innovative applications in financial markets contexts
