This is a hands-on role that also carries solution-architect responsibility: the candidate is expected to design end-to-end ML solutions - from data access and large-scale feature computation through training, inference, and production monitoring - on AWS, using services such as SageMaker (Training, Processing, Pipelines, Endpoints), S3, Athena, AWS Glue, EMR/Spark, Lambda, Step Functions, ECS/Fargate, Timestream, Aurora/RDS, Amazon Bedrock, and CloudWatch. Work closely with MLOps and Data Engineering teams to ensure smooth deployment of models, feature pipelines, data contracts, and other ML solutions in production environments, including CI/CD for ML, model versioning and registry, IaC (CloudFormation/CDK/Terraform), containerization (Docker/ECR), and production monitoring and alerting via CloudWatch, and participate in production validation and troubleshooting.