Built and configured a document augmentation pipeline using the Augraphy library for various document formats(DOCX, PDF, EML) and with OCR compatibility

Modeled, designed, and iterated on more than 20 high-fidelity dashboard wireframes and UI screens in Figma for the ShadowMaker application, including user workflows, logs views, and data visualization graphs

Wrote a technical research report for DRDC based on documentation for the ShadowMaker redaction tool, with detailed information on model capabilities, inference handling and diagrams

Implemented, trained, and tested an optimized 40M parameter document segmentation model(DocStructNet), with 97% accuracy on the DocLayNet and PubLayNet datasets

Used the AWS fmeval library, along with ECS, EC2, S3 and SageMaker to deploy a government chatbot with an evaluation pipeline

Reworked a Transport Canada Protected B application to replace AzureBlobService with a local version, and produced an exhaustive report on its functionality and security

Completed tickets in an Azure DevOps environment, interfacing with Azure GPU VMs, Azure Container Registry, and Azure SQL Database

Finetuned an open source LLM from the Hugging Face model library that enabled GPU costs to be reduced by 90%

Cleaned CIS Controls data from SCF, SOC II, CMMC and other cybersecurity frameworks

Used LangChain and HuggingFace embeddings to build a file-based chatbot that was able to effectively interact with clients and respond to queries

Studied Figma best practices and built several mock-ups to present to the client

Translated Figma components into functional React Native components

Used a provided REST API to integrate live data into the app