Data and Business Intelligence Engineer with 4+ years of experience at Amazon, METRO Markets, and several startups. Delivered solutions, lead engineering teams, and managed customers across accounting, risk compliance, and supply chain organisations. Specialised in Python, SQL, and 20+ AWS services for data, software, and GenAI automations. Master’s degree in Computer Science and AI from the University of Edinburgh.
Business Intelligence Engineer II in Accounting Technologies, working on Python and AWS automation projects.
Master's degree in Computer Science and Artificial Intelligence from the University of Edinburgh.
Building GenAI infrastructure and applications using RAG knowledge bases and MCP servers at Amazon.
Due to company and university policies, all sourcecode and sensitive details 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, MCP server, 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 ICU patients' mortality
predictions (98% accuracy) and length of stay predictions (96%
accuracy).
Python, TensorFlow, Scikit-Learn, Pandas, 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 data analysis, machine learning, and deep learning techniques for human
activity
recognition, clustering, process
mining, and activity forecasting, across over 5 years of daily
sensor data.
Python, TensorFlow, Scikit-Learn, Pandas, 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
