Designed and deployed scalable data and ML pipelines using AWS SageMaker, Airflow, and Snowflake to create and optimize inference workflows
Built data applications with Streamlit for data drift and model performance monitoring
Containerized applications using Docker and automated infrastructure with Terraform ensuring reproducibility and scalability across cloud environments
Developed and managed data pipelines in AWS, integrating Snowflake for high-performance data processing and analytics
Orchestrated complex workflows with Apache Airflow, optimizing scheduling, monitoring, and execution of data engineering and ML tasks
Collaborated with data scientists and engineers to productionize ML models, ensuring scalability, reliability, and seamless integration with business applications
Developed big data processing workflows with Spark and Airflow
