AI Cloud Engineer

Cloud Platform Expertise : Proficiency in cloud platforms (such as AWS, Azure, Google Cloud) and services tailored for AI workloads, such as AI / ML services, serverless computing, and Kubernetes-based solutions.

Infrastructure as Code : Implementing infrastructure as code (IaC) principles using tools like Terraform or CloudFormation to automate the provisioning and management of cloud resources.

Containerization : Deploying AI applications and services in containers (e.g., Docker) and managing container orchestration using Kubernetes or similar platforms for scalability and portability.

Security and Compliance : Ensuring AI solutions deployed on cloud platforms adhere to security best practices, compliance requirements, and data privacy regulations.

Integration and API Management : Integrating AI models with other cloud services and managing APIs for data ingestion, model inference, and result output.

Cost Optimization : Optimizing cloud resource usage to minimize costs while maintaining performance and scalability of AI workloads.

DevOps Practices : Implementing DevOps practices for continuous integration, continuous deployment (CI / CD), and automated testing of AI applications in cloud environments.

Support and Maintenance : Providing ongoing support, troubleshooting, and maintenance of AI infrastructure and applications deployed on cloud platforms.

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