Implemented the full-stack solution with a Java backend (using Conjure and Gatekeeper) and a React/TypeScript frontend with Redux for state management, deployed via a CircleCI, Gradle, and Apollo pipelines.

Improved stability of the Slate platform by debugging critical bugs, utilizing Grafana for performance metrics and Loki for log aggregation and analysis, and correcting memory leaks on testing pipelines.

Reduced monthly report production times by 90% by automating processing of Position and Cash Flow Statements for USD 700M in assets using Python, OpenAI APIs, Openpyxl, Pandas and implemented transaction type detection capabilities for purchases, divestments, capital calls, distributions, and fees across all statements.

Improved data-driven decision-making for bond allocations, by analysing the fund’s USD 180M fixed income portfolio that informed investment strategy presentations to the principal.

Accelerated deployment of the Bloomberg PORT Enterprise by 2 months, by developing custom scripts to port 1.5 years of data to the platform.

Reduced report production times for given ticker codes by 80% and improved quality of analysis of reports by developing automated market analysis systems using Interactive Brokers API, LlamaIndex, LangChain, and OpenAI APIs, improving ability to make more informed decisions on assets.