Improve observability, testing, performance and organisation of financial modelling codebase.

– Enhanced monitoring by introducing more tests with mocked data, and by improving the range of instruments covered, ensuring timely alerts for data generation failures, reducing downtime and lost trading opportunities.

– Investigated performances issues in library, speeding up specific use-case three-fold.

– Cleaned up 1000-file codebase: tested, improved and fixed 100 applications; merged various scripts together without impacting system stability.

Primary task: improve test coverage and testing infrastructure for AI codebase.

– Improved performance of Docker CI pipeline four-fold, leading to faster development. Improved test case coverage by 11%, developed multiple test fixtures.

– Developed stress testing tool by spinning up thousands of simultaneous connections. Uncovered critical bugs within the system.