Ambitious business intelligence and data engineer with 3+ years of experience across Amazon FinTech / Accounting Technologies and Operations, METRO Markets, and several start-ups. Specialised in Python, AWS, and SQL for data pipelines, machine learning, and metrics automation. Master’s degree in computer science and AI from the University of Edinburgh, specialising in AI for healthcare.
Business Intelligence Engineer in Accounting Technologies, working on Python and AWS automation projects.
Amazon GenAI and agentic AI hackathon applications using AWS RAG knowledge bases and MCP servers.
Completed my master's degree in AI and computer science from the University of Edinburgh.
Due to company and university policies, all sourcecode and sensitive details must remain private
In a team of 4, we developed an AWS Lex chatbot using Bedrock, a RAG knowledge base, API
Gateway, Amplify, S3, and Lambda to add documents to the RAG model.
The chatbot answers questions about Amazon domains, based on documents uploaded by the user
through the UI.
AWS Bedrock, API Gateway, Amplify, S3, Lambda, Lex, React, Python, JavaScript
In a team of 5, we developed an internal Slack bot that uses AWS Bedrock, a RAG knowledge
base, API Gateway, Lambda, S3, and OpenSearch to answer users' questions about a Slack
channel's history.
New Slack channels are ingested into the knowledge base through a Slack bot interface,
allowing onboarding to new channels.
AWS Bedrock, API Gateway, Lambda, S3, OpenSearch, Slack
Interpretable and Trustworthy Machine Learning for ICU Admission Length of Stay and
Mortality Predictions, supervised by Dr Sohan Seth. Using machine
learning and data science techniques for hospital patients' mortality
predictions and length of stay predictions.
Python, TensorFlow, Scikit-Learn, PM4Py, Prophet, NeuralProphet, Git
Detecting Long-Term Deviation and External Factors Correlations in Activities of Daily
Living Based on Sensor Data, supervised by Dr Jacques Fleuriot. Using machine
learning and deep learning techniques for human activity recognition,
clustering, process
mining, and activity forecasting.
Python, TensorFlow, Scikit-Learn, PM4Py, Prophet, NeuralProphet, Git
In a team of six, we developed a full-stack web search engine for 10m Spotify
podcast transcript snippets, enabling users to search for and listen to only
the parts of
podcasts and topics they are interested in. Implemented features include ranked information
retrieval, semantic
search, topic modelling, query expansion, and CI/CD using Jenkins via GitHub.
Python, Flask, React, PostgreSQL, MongoDB, Docker, Google Cloud, Jenkins
We developed a full-stack mobile app for real-time human activity
recognition from wearable sensors in a team of three. We used deep learning to
achieve classification accuracies of 94% for 4 activity subsets and 72% for 14 different
activities. The app shows the classified activity, step count, and stores historical user
data using Firebase.
Python, TensorFlow, Keras, Scikit-Learn, Java, Kotlin, Firebase, Git
I am a member of AIML, the Artificial Intelligence Modelling Lab research group at the University of Edinburgh's AIAI institute (Artificial Intelligence Applications Institute). I held two talks about my dissertation's research goals, proposed methods, and concluded findings as part of my viva preparations. I also regularly attended other research talks, learning about state-of-the-art machine learning applications.
