Enterprise Software Engineer (Junior)

Role Overview: As a Private Cloud AI Platform Engineer at Qubrid AI, you will be responsible for working on the software that powers the on-prem AI platform. Your main tasks will involve developing features that simplify deployment, management, monitoring, and operation of AI infrastructure, GPU clusters, and AI models in enterprise environments. The ideal candidate for this role will enjoy building products that combine software engineering, cloud-native technologies, infrastructure automation, Linux systems, networking, and AI infrastructure. This hands-on engineering role requires robust coding skills and a practical understanding of enterprise infrastructure environments. Responsibilities: - Platform Development: - Develop and enhance Qubrid's on-prem AI platform and management software. - Build enterprise-grade platform features for AI infrastructure management. - Design and develop APIs, backend services, and platform integrations. - Create software that simplifies deployment and management of AI workloads in customer environments. - Build self-service workflows for infrastructure and model deployment. - Enterprise Platform Features: - Develop user management, role-based access control (RBAC), and multi-tenancy capabilities. - Implement SSO, LDAP, Active Directory, and SAML integrations. - Build audit logging, monitoring, alerting, and operational dashboards. - Develop upgrade, patch management, and lifecycle management capabilities. - Support enterprise security and compliance requirements. - Infrastructure & Automation: - Work with Kubernetes-based deployments and orchestration systems. - Automate installation and configuration of AI infrastructure. - Develop cluster provisioning and management workflows. - Build software for monitoring GPU, compute, networking, and storage resources. - Integrate with cloud and hybrid cloud environments. - AI Platform Integration: - Integrate AI inference services into the platform. - Support model deployment, management, and lifecycle workflows. - Develop APIs and services for AI applications and model serving. - Enhance observability and operational management of AI workloads. Required Qualifications: - Bachelor's degree in Computer Science, Engineering, or related field. - 2+ years of software development experience. - Strong Python development skills. - Experience building backend systems and APIs. - Experience with Linux administration and troubleshooting. - Understanding of networking fundamentals including TCP/IP, DNS, routing, firewalls, VLANs, and load balancing. - Experience with Docker and containerized applications. - Familiarity with Kubernetes and cloud-native technologies. - Strong problem-solving and debugging skills. Role Overview: As a Private Cloud AI Platform Engineer at Qubrid AI, you will be responsible for working on the software that powers the on-prem AI platform. Your main tasks will involve developing features that simplify deployment, management, monitoring, and operation of AI infrastructure, GPU clusters, and AI models in enterprise environments. The ideal candidate for this role will enjoy building products that combine software engineering, cloud-native technologies, infrastructure automation, Linux systems, networking, and AI infrastructure. This hands-on engineering role requires robust coding skills and a practical understanding of enterprise infrastructure environments. Responsibilities: - Platform Development: - Develop and enhance Qubrid's on-prem AI platform and management software. - Build enterprise-grade platform features for AI infrastructure management. - Design and develop APIs, backend services, and platform integrations. - Create software that simplifies deployment and management of AI workloads in customer environments. - Build self-service workflows for infrastructure and model deployment. - Enterprise Platform Features: - Develop user management, role-based access control (RBAC), and multi-tenancy capabilities. - Implement SSO, LDAP, Active Directory, and SAML integrations. - Build audit logging, monitoring, alerting, and operational dashboards. - Develop upgrade, patch management, and lifecycle management capabilities. - Support enterprise security and compliance requirements. - Infrastructure & Automation: - Work with Kubernetes-based deployments and orchestration systems. - Automate installation and configuration of AI infrastructure. - Develop cluster provisioning and management workflows. - Build software for monitoring GPU, compute, networking, and storage resources. - Integrate with cloud and hybrid cloud environments. - AI Platform Integration: - Integrate AI inference services into the platform. - Support model deployment, management, and lifecycle workflows. - Develop APIs and services for AI applications and model serving. - Enhance observability and operational management of AI workloads. Required Qualifications: - Bache