• Designed, deployed, and maintained all cloud infrastructure on AWS supporting the backend of an app with 100k+ downloads and 20k+ users .
• Designed all data models, including all transactional SQL and NoSQL databases as well as analytical Data Warehouses
• Designed and Deployed an internal vector database using HNSW indexes in FAISS which created a solution that was 200% faster and 120% cheaper than Milvus, Pinecone or other similar alternatives
• Developed the core ML algorithms in Python powering the main features in our app, including 'Image-Capture' and our personalized recommendation algorithm designed to revolutionize art exploration.
• Integrated Google Gemini Multimodal LLM into the upload pipelines, automating content quality control.
• Architected serverless infrastructure on AWS , hosting various custom ONNX models as well as event-driven backend microservices.
• Led a team of engineers, supervising essential projects and planning the development cycle.
• Secured important tech partnerships with AWS and DataStax.

Successfully completed 10+ different Python projects ranging from simple web-scraping requests to more challenging data-mining tasks.

Implemented a web scraping script with and interactive GUI as well as a basic recommender system for lead generation in Python. It was used to automatically update a small Google Sheets database with new leads, which helped reduce the number of employees doing the same task manually, thus, saving a considerable amount of money and resources.

Final Dissertation (Grade: 76): 'Theoretical And Practical Investigations Of The Travelling Deliveryman Problem'

First Class Honours

Final Project (Grade: 76): 'Sufficient Dimension Reduction in Support Vector Regression'