+ Design and manage cloud infrastructure using AWS services such as EKS, EC2, SageMaker, Lambda, and S3.
+ Automate cloud resource provisioning and configuration using Terraform and Infrastructure as Code (IaC) best practices.
+ Implement CI/CD pipelines for ML model deployment using GitHub Actions, Jenkins, or AWS CodePipeline.
+ Monitor and optimize ML workflows in Databricks, ensuring performance, scalability, and reliability.
+ Collaborate with data scientists and product teams to operationalize ML models and integrate them into production systems.
+ Develop robust APIs for model serving using FastAPI, supporting synchronous, asynchronous, and streaming inference.
+ Build and maintain front-end interfaces using React to visualize model outputs, monitor pipeline health, and support user interaction with ML services.
+ Apply software engineering principles to develop scalable, maintainable, and testable code across the ML lifecycle.
+ Support deployment and or...