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I am a Machine Learning Engineer with over three years of experience delivering production ML solutions in healthcare, ad-tech, and insurance sectors. I've driven throughput improvements of 10x–14x, achieved cost reductions up to 80%, and implemented full lifecycle automation including retraining, drift monitoring, and QA using MLflow and Airflow on platforms like GCP and Databricks. My expertise spans Spark, Python, SQL, Docker, and Kubernetes, and I've deployed real-time PII redaction and retrieval-augmented generation agents. I enjoy solving complex problems and applying advanced MLOps and cloud technologies to enhance scalable machine learning systems across industries.…I am a Machine Learning Engineer with over three years of experience delivering production ML solutions in healthcare, ad-tech, and insurance sectors. I've driven throughput improvements of 10x–14x, achieved cost reductions up to 80%, and implemented full lifecycle automation including retraining, drift monitoring, and QA using MLflow and Airflow on platforms like GCP and Databricks. My expertise spans Spark, Python, SQL, Docker, and Kubernetes, and I've deployed real-time PII redaction and retrieval-augmented generation agents. I enjoy solving complex problems and applying advanced MLOps and cloud technologies to enhance scalable machine learning systems across industries.

Geoffrey Wong Hin

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I am a Machine Learning Engineer with over three years of experience delivering production ML solutions in healthcare, ad-tech, and insurance sectors. I've driven throughput improvements of 10x–14x, achieved cost reductions up to 80%, and implemented full lifecycle automation including retraining, drift monitoring, and QA using MLflow and Airflow on platforms like GCP and Databricks. My expertise spans Spark, Python, SQL, Docker, and Kubernetes, and I've deployed real-time PII redaction and retrieval-augmented generation agents. I enjoy solving complex problems and applying advanced MLOps and cloud technologies to enhance scalable machine learning systems across industries.…I am a Machine Learning Engineer with over three years of experience delivering production ML solutions in healthcare, ad-tech, and insurance sectors. I've driven throughput improvements of 10x–14x, achieved cost reductions up to 80%, and implemented full lifecycle automation including retraining, drift monitoring, and QA using MLflow and Airflow on platforms like GCP and Databricks. My expertise spans Spark, Python, SQL, Docker, and Kubernetes, and I've deployed real-time PII redaction and retrieval-augmented generation agents. I enjoy solving complex problems and applying advanced MLOps and cloud technologies to enhance scalable machine learning systems across industries.

Available to hire

I am a Machine Learning Engineer with over three years of experience delivering production ML solutions in healthcare, ad-tech, and insurance sectors. I’ve driven throughput improvements of 10x–14x, achieved cost reductions up to 80%, and implemented full lifecycle automation including retraining, drift monitoring, and QA using MLflow and Airflow on platforms like GCP and Databricks.

My expertise spans Spark, Python, SQL, Docker, and Kubernetes, and I’ve deployed real-time PII redaction and retrieval-augmented generation agents. I enjoy solving complex problems and applying advanced MLOps and cloud technologies to enhance scalable machine learning systems across industries.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

Research Assistant at Shatin Hospital

August 1, 2024 - August 23, 2025

Increased diagnostic model AUC to 73% by applying advanced feature engineering and data augmentation on medical imaging data. Developed autonomous agents in LangChain that queried SQL databases and generated dynamic visual reports with a 95% task success rate. Cut real-time PII redaction costs by 80% through integrating pretrained face-masking computer vision models and large language models into production ML pipelines.

Data Scientist at Hotmob Limited

June 1, 2023 - August 23, 2025

Improved NLP system throughput by 10x and halved inference costs by fine-tuning GPT-2 and deploying optimized serving infrastructure on GKE for automated website content understanding. Accelerated ETL speed 14x and reduced memory use significantly by implementing a max-connected component algorithm for scalable ID graph matching. Automated ML retraining and drift detection workflows saving up to 3 hours of manual daily labor. Re-platformed legacy Apache Beam pipeline into modular, testable components with CI/CD, achieving 80% code reduction and 67% runtime reduction with fault tolerance and backfills. Led MLOps adoption by migrating workflows from SAS to Databricks and building Power BI dashboards for performance monitoring.