Singapore
I am an AI and data practitioner with industry experience in delivering end-to-end classical machine learning and Generative AI projects. I am also a strong advocate for continous learning and self improvement, eager to acquire new technologies and techniques related to AI and software engineering as a whole. I believe that the key to any successful AI project is to have deep domain knowledge of the data and landscape, and understanding how end users can integrate AI solutions to augment existing processes and workflows to drive value creation. Outside of work, I am also an enthusiastic musician and educator!
- Awarded Drum Educator of the Month in July 2016, and Drum Educator of the Quarter in 2020 Q1 - Average service rating 9.5/10 based on students feedback on lesson quality
- Collaborated with a cross functional team in an Agile environment to develop a proof of concept platform for residents to create Healthier SG diet and exercise plans. Implemented an agentic workflow that demonstrates flexibility to curate and refine personalised plans that residents can act on to lead healthier lifestyles - Successfully developed and implemented backend microservices for a cybersecurity self-attestation portal augmented with Generative AI for a health technology agency, assisting external vendors in their cybersecurity policy compliance to onboard to the healthcare ecosystem. Improved vendor compliance and attestation productivity - Developed a chat engine feature within a carbon market data management platform for an overseas environmental intelligence client to process natural language queries and retrieve multi-dimensional insights from structured SQL tables and PDF documentation. - Provided sharing of knowledge in internal Brownbag sessions (agentic RAG workflows, evaluation metrics for LLMs) - Organised offsite team bonding events for continuous workplace engagement
- Completed 2 months of deep skilling on machine learning fundamentals. Topics covered includes classification, artificial neural networks and deep learning techniques in CV, NLP, and time-series tasks - Collaborated with AI engineers and apprentices to deliver a 7 months long project to an external client, developed a minimum viable model and an end-to-end pipeline deployed into production. - Developed code base for key stages in pipeline, including data preparation, feature engineering, model training and inferencing. Contributed to code reviews and picked up best practices in software engineering and code cleanliness - Planned and executed experimentation of model hyperparameters and features using MLFlow, evaluating model performance on key metrics based on client's business needs - Developed and containerized pipelines for data preparation (cleaning and feature engineering), model training/evaluation and batch inference for deployment - Developed and implemented unit tests to maintain integrity of code base as part of CI/CD process - Presented data insights and model interpretability to client during sprint reviews as part of project deliverables to explain model predictions