experiences

software development engineer

enhancing discover’s file transfer platform (dftp), orchestrating and optimizing the secure transmission of over 30,000 internal and external files daily by improving system reliability, performance, and mitigating real-time issues.

– datadogs, gpfs, python, shell scripting, jenkins, and unix.

developed and implemented machine learning models for anomaly detection, leveraging k-means clustering, isolation forest, and hbos algorithms, achieving a 52\% reduction in anomalous client behaviors.
architected and deployed a high-performance anomaly detection API, uncovering 26\% more irregularities, and mitigating risks valued at up to $1,000,000.
designed an client feedback loop for the ml system, enabling dynamic model refinement based on user input, which drove a 30\% increase in detection accuracy and facilitated personalized anomaly thresholds for individual clients.

ptimized and expanded an existing AWS infrastructure for Ecobee’s data protection and security by reconfiguring AWS and Lambda functions, saving an estimated $200,000 annually.
devised a ci/cd pipeline using jenkins, python 3.7, and shell scripts, automated routine unit testing for carrier’s 2023 smart thermostat by 43%, thereby replacing the carrier comfort network (ccn). reduced time-to-market by 15%.

authored over 2,500 lines of structured python code and attained a 37% reduction in transaction processing time. improved efficiency led to a 15% increase in daily transactions, boosting revenue by an estimated $60,000 annually.