AI Cloud Engineer

TECH

Hybrid

Full-Time

Barcelona

  • 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.

  • 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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