SRM-AI&ML Analytics Platforms Engineer

Responsibilities

• 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.