Cloud computing, distributed systems, infrastructure as code, web

Mobile:

• Designed and implemented a cross-platform, multi-modal dialog system. Collaborated with Unity Engineers to develop and integrate the dialog system for educational games

• Developed Android and iOS modules for cloud services, cross-platform interfaces, database/storage, machine learning, and core AI features

• Tech Stack: C++, Swift, Obj-C/C++, Java, Kotlin, C#, Bazel, Clang, CMake, Gradle, JNI, RxCpp, RxJava, RxSwift, MLKit, Kaldi, Google Cloud ASR, Couchbase, Firebase, Realm, Cocoapods, Maven, gRPC

Infra:

• Developed and updated AI cloud services i.e: integration of machine learning features, database server, and principal system logic

• Setup CI/CD server and pipelines for mobile, web, and internal applications

• Implemented internal tools to help minimize developer bottlenecks and improve stakeholder knowledge

• Tech Stack: C++, Python, Java, Obj-c/c++, Bazel, RxCpp, gRPC, Protobuf, PIP, Wheel, PyPI, Twine, Textmate, Azure, Jenkins, Bash, SSH

Web:

• Maintained web development environment (IDE) used to write and deploy proprietary language scripts

• Tech Stack: Javascript, PugJS, jQuery, HTML/CSS, Webpack, Node, gRPC, Firebase, GCP, Bazel

• Developed real-time audio/video streaming application, which automated reading examinations

• Implemented machine learning models to process educational videos and generate training data as code-blocks

• Performed analysis on text to automate grading of written responses and generate statistical reports on student performance

• Tech Stack: Python, Go, Node, WebRTC, RTSP, Spacy, gloVe, nltk, pyMC3, OpenCV, Google ASR, Numpy, Android, Firebase, VM, quagga

• Wrote Python scripts to analyze and model hand movement.

• Applied supervised machine learning methods to predict hand poses given the orientation of joints.

• Reduced computation cost by creating a revised model that minimized data and run-time needed to make inferences.