Machine Learning Engineer

• Design, implement, and maintain scalable cloud infrastructure for AI applications using AWS services, with a focus on ECS, S3, RDS, and Bedrock
• Manage deployments with Kubernetes clusters, ensuring high availability and performance, and integrating with our existing infrastructure best practices
• Develop and maintain CI/CD pipelines
• Develop and expand our Infrastructure-as-Code (IaC) with Terraform to ensure consistent and reproducible deployments across multiple environments
• Collaborate with Python developers to containerize AI applications and optimize them for production deployment
• Design and implement cloud networking solutions that support secure and efficient communication between AI microservices
• Maintain and monitor security best practices for AI applications, including secrets management, access controls, and compliance requirements
• Troubleshoot and resolve infrastructure-related issues across the AI application stack, ensuring minimal disruption to business operations
• Stay updated with emerging MLOps tools, cloud technologies, and industry best practices to continuously improve our deployment and infrastructure capabilities
• Participate in the strategic planning of AI Innovation infrastructure, identifying opportunities for automation and process improvement

MLOps Engineer – AI Innovation

AI Architect