Worked on the DataDog/watermarkpodautoscaler repository, focusing on backend stability and API correctness. Addressed a critical bug in the recommender component where fractional watermark values below 1.0 were incorrectly truncated due to invalid float conversion. Using Go and leveraging expertise in API development and Kubernetes, implemented and validated a fix that ensures accurate representation of fractional watermarks in the recommender API. This change improved the reliability of recommendation signals and reduced downstream edge cases. Emphasized code health through thorough debugging, review, and validation practices, prioritizing long-term maintainability and reducing the risk of future regressions without introducing new features.