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Anton Papadimitriou

@antonpapadimitriou

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Senior AI/ML engineer specializing in scalable healthcare and e-commerce solutions.

United States

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What I'm looking for

I seek roles building scalable, production ML/AI systems in healthcare or e-commerce, with strong engineering teams, mentorship opportunities, and emphasis on data security and measurable impact.

I am a Senior AI/ML engineer with eight years building production ML systems for healthcare and e-commerce, holding a PhD in Computer & Information Science from the University of Pennsylvania. I design and deploy deep learning, recommendation, and anomaly-detection solutions using TensorFlow, PyTorch, AWS, and modern MLOps practices to deliver measurable business impact.

My work includes leading patient risk stratification and virtual assistant projects that improved predictive accuracy and user satisfaction, and architecting recommendation and fraud-detection systems that raised engagement and reduced losses. I mentor teams, ensure HIPAA-compliant data security, and focus on scalable, reliable deployments using containerization and cloud-native services.

Experience

Work history, roles, and key accomplishments

Senior AI Engineer

One Medical

May 2023 - Dec 2025 (2 years 7 months)

Led development and deployment of patient risk stratification and AI virtual assistant systems using XGBoost, LSTM, BERT and GPT, achieving 85% precision and a 15% increase in user satisfaction while ensuring HIPAA-compliant, cost-optimized cloud deployments.

XGBoost BERT AWS Sagemaker Docker EKS HIPAA RESTful APIs

Senior Machine Learning Engineer

Overstock

Jul 2018 - Apr 2023 (4 years 9 months)

Led recommendation engine and fraud detection initiatives using collaborative filtering, matrix factorization, Random Forests and GBMs, increasing sales conversion by 12% and improving fraud response times by 25% via real-time ETL and streaming pipelines.

Collaborative Filtering PyTorch Apache Spark Kafka Random Forest scikit learn Docker Kubernetes

Applied Scientist

Microsoft

Jul 2017 - Jun 2018 (11 months)

Improved real-time video background segmentation using CNNs (VGG16/ResNet) in TensorFlow, increasing accuracy by ~7–10% and delivering automated testing suites to validate performance across environments.