At T. Rowe Price, I worked as a Full Stack / AI/ML Engineer, where I developed AI-driven financial applications integrating real-time data analytics and predictive models. Using React.js and Node.js, I built scalable web interfaces powered by GraphQL APIs, providing portfolio managers with actionable insights. I also developed machine learning models in TensorFlow and PyTorch for predictive analytics and NLP tasks, processing large datasets with Pandas, NumPy, and Apache Spark. I deployed models using AWS Sagemaker, MLflow, and Kubernetes, automating model lifecycles and ensuring scalability through Docker and Terraform. This work enabled real-time financial decision-making and enhanced investment strategies.

As an AI/ML Engineer at Prealize Health, I developed and deployed innovative machine learning solutions to enhance healthcare outcomes and streamline internal processes. I designed machine learning models using TensorFlow and PyTorch for tasks such as medical image classification and disease risk prediction, optimizing them for accuracy with large datasets of patient records and medical images. I implemented natural language processing (NLP) techniques using Hugging Face Transformers and SpaCy to analyze clinical notes and research papers, extracting key insights to support healthcare professionals. My work included leveraging Pandas, NumPy, and Apache Spark for predictive analytics to identify patterns in patient outcomes, as well as developing deep learning models with Convolutional Neural Networks (CNNs) for automating disease detection in medical imaging. I deployed these models on AWS Sagemaker and Google AI Platform, utilizing MLflow and Kubeflow for effective model management. By collaborating closely with medical teams, I translated clinical requirements into scalable AI solutions, ultimately contributing to better patient care and data-driven decision-making in healthcare.

As a Software Engineer at Accenture, I worked on modernizing a digital advertising platform for a Fortune 500 client by developing a data lake-house infrastructure using AWS. I built and optimized ETL pipelines with Apache Spark and Databricks to process large-scale advertising data, and integrated AWS Redshift for efficient data analytics. Using R and Python, I collaborated with the Data Science team to implement AI/ML models for customer segmentation and personalized targeting, utilizing technologies like scikit-learn and TensorFlow. I also containerized microservices with Docker and Kubernetes for scalable deployments, ensuring data governance and security compliance, which improved platform efficiency and data-driven decision-making.