Software Engineer and AI Ethics specialist with 8+ years’ experience developing ML & software systems responsibly and at scale. Combining strong technical expertise with a master’s in AI Ethics from the University of Cambridge, I bring both the engineering depth and ethical insight needed to ensure AI systems are robust, compliant, and aligned with responsible AI principles.
Recent experience
Sky
Software Engineer, MLOps
Oct 2020 - PresentDesigned and built a platform to deploy ML models at scale into production, serving millions of customers, while ensuring traceability and auditability across the ML lifecycle. Developed and maintained an open-source custom Kubernetes operator for Kubeflow Pipelines, integrating model metadata, lineage and auditability to support governance processes. Presented internally on Model Cards and explainability, promoting transparency and responsible ML development practices. Actively contributed to Sky’s Data Ethics Network, applying insights from AI Ethics degree. Using Go, Python, Scala, TensorFlow, TensorFlow Serving, TFX, Kubeflow, and gRPC.
Developer II
Mar 2020 - Oct 2020Produced high quality production code whilst expanding the NOW TV application to several territories. Mentored junior developers, guided technical discussion, and established best engineering practices across teams.
Developer
Dec 2018 - Mar 2020Developed and maintained NOW TV client applications for smart TVs and the web, enhancing features, fixing bugs, and optimizing CI pipelines. Led macOS notarization for Catalina compliance. Primarily worked with React/Redux.
Associate Software Developer
Jul 2017 - Dec 2018Implemented a prototype Alexa skill/Google Home action, gaining experience with NodeJS and Alexa/Google Home APIs.
Summer Placement, Software Engineering Academy
Aug 2016 - Sep 2016Freelance work
Education
University of Cambridge
MSt AI Ethics and Society (1st Class)
Sep 2021 - June 2023Multidisciplinary master’s degree covering AI Ethics, philosophy, history, law, and social impact. Dissertation on open-source tools for explainable machine learning, focusing on how SHAP, LIME, Model Cards, AIX360, and similar frameworks influence accountability in AI systems. Developed a practical understanding of responsible AI governance, risk frameworks, and emerging regulation such as the EU AI Act and ISO standards.
Springboard
Machine Learning Engineering Career Track
Sep 2019 - May 2020Intensive online course covering the fundamentals of machine learning, deep learning, NLP, and CV. Also involved a capstone project to design, develop and deploy a completely scalable machine learning system. Gained skills in Python, Scikit Learn, PyTorch, and end to end machine learning.
University of Manchester
BSc Computer Science (Hons) (1st Class)
Sep 2014 - Jun 2017Overall grade: 79%. Third Year Project: 85%
Projects
- Traffic Light Recognition: Detection of Traffic Lights using Faster RCNN architecture, written with Pytorch, deployed using AWS Elastic Beanstalk.
- Tracking Football Players: Tracking football players in low quality video using traditional Computer Vision techniques.