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• Leveraged GitHub Actions to automate the deployment pipeline for machine learning models, ensuring efficient and consistent delivery across development, staging, and production environments.

• Integrated GitHub Actions with existing CI/CD processes to automate testing, packaging, and deployment of machine learning models, reducing manual intervention and accelerating time to market.

• Managed and monitored 350 ECS tasks across three distinct environments (development, staging, and production), ensuring the optimal performance and availability of machine learning models hosted on AWS ECS cluster

MLOps: Successfully implemented the machine learning infrastructure for the Transit Time and Service Time predictive services. Leveraging AWS SageMaker, I designed and deployed a highly scalable multi-model endpoint that enabled the services to predict customer delivery times with high accuracy.

Monitoring: Successfully led the effort to rename StatsD to better leverage Datadog services. My responsibilities included collaborating with cross-functional teams, developing a plan to refactor the codebase, and ensuring seamless migration to the new naming convention.