Senior AI Engineer

Directed the design and deployment of production-grade AI systems using Python, PyTorch, and AWS, analyzing 12M+ oncology patient records and improving clinical outcome prediction accuracy by 23% using real-world clinical data. Architected scalable ML data ingestion and feature engineering pipelines using Apache Spark and Airflow, accelerating clinical data processing by 40% across oncology research and real-world evidence (RWE) workflows. Implemented a model evaluation and validation framework using MLflow and distributed experimentation pipelines, reducing validation timelines by 60% across the ML development lifecycle. Deployed machine learning inference services via Docker and Kubernetes-based microservices, enabling AIdriven clinical decision support used by 60+ healthcare partners and research teams. Established MLOps monitoring and model governance pipelines using MLflow, Prometheus, and Kubernetes, reducing model drift incidents by 35% in production healthcare analytics systems. Mentored 6 AI engineers, guiding development of production ML pipelines, model deployment services, and scalable inference infrastructure supporting oncology analytics.

Designed predictive AI systems using Python, PyTorch, and XGBoost, analyzing 8M+ behavioral and transaction signals to improve fraud and risk detection accuracy by 22%. Built large-scale ML experimentation and hyperparameter optimization pipelines using Apache Spark and MLflow, increasing model performance by 19% across multiple ML models. Reduced deep learning training cycles by 40% through distributed training infrastructure using PyTorch and GPU clusters on AWS. Implemented experiment tracking and model validation workflows using MLflow and Airflow, integrating cross-validation, performance scoring, and evaluation dashboards to shorten testing cycles by 55%. Deployed real-time machine learning inference services using Docker and Kubernetes, powering predictions for millions of platform users and reducing decision latency by 30%. Mentored 5 machine learning engineers, strengthening development practices across feature engineering, model deployment, and MLOps workflows.

Developed scalable distributed machine learning pipelines using Apache Spark on Amazon Web Services, processing 100M+ mobility events to improve demand forecasting precision by 12% and reduce prediction error by 8%. Applied advanced predictive modeling techniques using XGBoost and Scikit-learn, increasing demand prediction accuracy by 17%. Analyzed behavioral and mental health datasets containing 400K+ anonymized user records to support predictive analytics and digital mental health AI research. Implemented model training and experimentation pipelines using PyTorch, accelerating ML experimentation and model iteration cycles by 35%. Conducted large-scale algorithm benchmarking and model evaluation to optimize route prediction and mobility demand forecasting models. Integrated machine learning models into internal data analytics and operational decision platforms, enabling datadriven insights for operations and data science teams.

Analyzed behavioral and mental health datasets containing 400K+ anonymized user records to support predictive analytics and digital mental health AI research. Implemented classification models using Scikit-learn, improving baseline model accuracy by 13%. Performed data preprocessing and feature engineering using Pandas and Python, improving data quality for ML training pipelines. Evaluated model performance through cross-validation, precision, and recall metrics, increasing reliability of experimental results. Produced analytical visualizations using Matplotlib to identify behavioral trends and mental health indicators. Assisted senior engineers in prototyping early-stage machine learning models for digital mental health support systems.