• Class 2 (Software) medical application that used ML to predict hyperglycemia episodes.

• Automated microservice deployment to AWS using Elastic Container Services to increase developer productivity.

• Converted existing ML codebases to fask microservices with a ReST API.

• Containerised existing services into docker containers for AWS deployment.

• Documented design and security decisions to comply with regulator requirements.

I was a software engineering intern on the machine learning team. During this internship, I improved my knowledge of statistical techniques for modelling and analysing data and put some of the concepts I have studied in my university machine learning course into practice.

• forecasted demand for Ingenuity Cloud Services based on past sales data. The forecast was used to predict net profit and server surplus numbers based on initial starting conditions.

• developed a personalised recommendation system using the NVIDIA merlin deep learning framework to consider the feasibility of using the tool to replace the existing recommendation system.

• developed and designed a GCP's Vertex AI kubeflow pipeline to automate the data processing pipeline and model training.

Worked for the university student software company. We laid the foundations for open sourcing a research application for automating data cleaning produced by the Department of Computer Science.

• Documented a large legacy Java codebase from the scratch

• Designed and planned how the application could be rearchitected to support a component pipeline-based approach with support for data streams as well as refactoring the code to Kotlin