Bona Chow
Machine Learning Engineer @Sage
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WORK HISTORY
Machine Learning Engineer @Sage
Large Scale Language Model Development: Supported development of transformer-based named entity recognition model trained on 27+ million documents, leveraging PyTorch Lightning for efficient experimentation across different model configurations and hyperparameter optimization- Production AI Systems: Developed and improved end-to-end ML pipeline from research to deployment, currently implementing production deployment using Triton Inference Server for high-throughput, low-latency inference- Distributed Training and Optimization: Implemented scalable training pipelines with advanced memory optimization and compute efficiency required for large-scale transformer training- Business Impact: Delivered document processing system supporting multimillion-pound commercial partnership, directly bridging research innovation with business-critical applications- International Collaboration: Co-operated with colleagues across teams spanning UK, Europe, US, and Israel.
EDUCATION
University of Cambridge
Master of Science (MSc), Natural Sciences
GIA (Gemological Institute of America)
Graduate Gemologist, Graduate Colored Stones, Graduate Diamonds, Graduate Pearls
City St George’s, University of London
Master's degree, Data Science
The Gemmological Association of Great Britain (Gem-A)
Gemmology Diploma (Distinction)
ABOUT BONA CHOW
Machine Learning Engineer specializing in Natural Language Processing (NLP), Transformer architectures, and production model deployment, with over 15 years of analytical research experience across scientific and data domains.At Sage (FTSE 100), I’ve spent the past three years developing and deploying NLP solutions at scale. My experience includes:• Contributing to the development of a transformer-based NER model trained on 27M+ accounting documents, improving automation accuracy across enterprise workflows.• Preparing and optimizing Dockerized ML services for deployment on Triton Inference Server, collaborating closely with ML infrastructure engineers.• Building a document extraction pipeline integrated into production APIs, supporting a multimillion-pound commercial partnership.• Developing Superset dashboards and automated alerts to monitor prediction performance and model accuracy over time.My MSc in Data Science (with Distinction) and prior research background in analytical chemistry and computer vision (with work featured on MDPI Minerals cover) demonstrate strong foundations in quantitative analysis and experimental design.Through studying AWS Solutions Architect Associate, AI Practitioner, Cloud Practitioner, Machine Learning Engineer Associate certifications, I have deepened my cloud and MLOps knowledge.As a multilingual practitioner (English, Cantonese, Mandarin, Japanese, German), I’m particularly passionate about LLMs and multilingual NLP, with an interest in addressing alignment and fairness challenges in low-resource languages.Outside of work, I mentor MSc students at Bayes Business School and City, University of London, helping bridge academic research and applied ML practice.
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