I build and operate data driven systems with a focus on reliability, automation, and production readiness. My experience spans data pipelines, analytics platforms, cloud based integration, and CI/CD workflows, where I enjoy turning raw data into systems that can be monitored, scaled, and trusted in real world environments.
I am currently pursuing a Master’s in Machine Learning and Mathematical Modelling, strengthening my foundation in applied machine learning, statistical modelling, and MLOps concepts. My long term interest lies in working at the intersection of data engineering and machine learning, contributing to practical, production ready solutions rather than purely experimental code.
- Strengthening fundamentals in machine learning, statistical modelling, and optimisation
- Building end to end ML workflows using Python (data preparation → modelling → evaluation)
- Learning MLOps concepts such as model versioning, monitoring, and deployment patterns
- Applying cloud and automation principles to data and ML pipelines
- Improving code quality, reproducibility, and experiment tracking
- Data Engineering: Data pipelines, integration patterns, analytics ready data models
- Analytics & Monitoring: SQL based analysis, dashboards, operational metrics
- Cloud & Automation: CI/CD workflows, containerisation, cloud platforms
- Machine Learning (Learning & Applied): scikit-learn, PyTorch, model evaluation, MLOps concepts