• Worked on the flutter infrastructure team making sure the release of monthly software updates

is deployed correctly and handle the scalability of the project in a healthy way serving 500,000

users monthly.

• Reduced the latency of the CI system from 15 minutes to ~5 seconds by using GitHub web hooks

to schedule testing instead of relying on a cron job.

• Improved runtime metrics by 2% for the smoke testing by changing the test strategy (Running a

smaller but still representative subset of tests)

• Collaborated with cross-functional teams to implement a pub/sub solution to reduce the CI

flakiness that reduce the SLO metric by 3%.

• Handled the stable release of flutter 3.19.0 and 3.16.0.

• Extended the flutter cli tool to surface anomalies in client applications and make

recommendations on how to repaired them.

• Optimized the flutter CI on GitHub by sharding their tests resulting in a reduction of ~1%

flakiness

Managed a team of 4 people for 5 years, responsible for extracting features and training machine

learning models to detect interference in cellular networks (LTE and 5G).

• Balanced priorities to meet release cycles; working around promises made to clients.

• Mentored new team members through codes review and weekly check ins. (1 on 1).

• Developed a data pipeline to train a machine learning model with an accuracy of 96%.

• Implemented a k-means model to cluster the cellular networks of over 3 major US cellular

carriers.

• Parsed binary data coming from clients each 15 minutes (~3 TB daily) with low latency using

multi-threading.

• Documented the redesign of a monolithic application into microservices using Kubernetes and

docker.

• Oversaw the migration of python 3.4 into 3.9, writing new tests that ensure backward

compatibility.