Employed prompt engineering techniques to scrutinize autogenerated responses within the context of data science, ensuring their reliability, and modifying them as needed to improve their accuracy. Primarily utilizing Python and R.

Addressing complex data challenges, optimizing algorithms for peak performance, and crafting robust test suites to ensure software reliability.

In very simple words, chatGPT asks me for help.

Optimizing Uber’s internal experimentation service by migrating the system’s API to a more efficient gRPC endpoint and incorporating evolved edge gateways for the engineers’ observability.

Developing cohesive data pods that minimizes field retrieval loss and increases storage efficiency for feature parameters.

Developed back-end API (via REST and GraphQL) inventory management web service for a virtual store, developed using agile techniques.

Responsible for designing and documenting the API and implementing the controllers, service, layering, repository, Java data objects, and unit tests for the application based on the provided database structure.

Constructed a UI implementation of the feature under the designated session or position. This project phase accounts for the transitions as the user navigates the different segues on their Android device.

Developed a seamless integration of a complex machine-learning model which masterminds the processing of the feature on which my project is based. I adapted the model, assembled a model-specific tensor-flow wrapper, and ensured compatibility with Google's Android devices.