Job description

Work with Data Science and BUs to define solutions for institutionalizing data-driven decision-making in a cost-effective and scalable manner.
Focus on driving analytical outcomes and impacting the Machine Learning and AI ecosystem.
Experience in AI/Client platforms such as AWS Sage maker.
Focus on optimizing existing systems, building infrastructure, and eliminating work through automation.
Influence application and security architecture and design across multi and hybrid cloud platforms.
Peer-reviewing infrastructure-as-code (AWS Cloud Formation, Python, Terraform, or similar).
Partnering with application and infrastructure teams to develop reusable cloud patterns.
Deployment and troubleshooting of infrastructure code.
Identify opportunities to build self-service capabilities and automate infrastructure and application deployments.
Develop tools and best practices for platform development, developer productivity, automation (MLOps, CI/CD, A/B testing), and production operations.
Design, develop and deliver critical components, frameworks, services, and products using AWS Sagemaker, Lambda, and container technologies in AWS.
Develop processes, model monitoring, and governance framework for successful client model operationalization.
Define standards for engineering and operational excellence for running best-in-class client platforms and continue to improve client platforms to keep up with the latest innovations.
Assist in gathering and analyzing non-functional requirements and translating that into technical specifications for robust, scalable, supportable solutions that work well within the overall system architecture.

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Job-Id-SRM-AI&ML Analytics Platforms Engineer

Job description

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