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Read everything carefully. The requirements and screening questions are critical and if not answered correctly and satisfactorily will result in auto-rejection and waste of your time. Work from Home. This is a full-time role. If you plan to do 2 or more jobs at the same time or want to do this part-time, that won't work for us. In that case please do not apply as it will get auto-rejected Note - this job requires working late night India time until 4 AM to overlap with USA working times. Do not apply if this timing doesn't work Salary depends on experience and current verifiable (paychecks) compensation. Junior candidates with 2 years experience are suitable Private Cloud AI Platform Engineer About Qubrid AI Qubrid AI is building a full-stack AI infrastructure platform that enables enterprises to deploy, manage, and scale AI workloads across cloud, on-premises, and hybrid environments. Our platform combines GPU infrastructure, AI model serving, inference APIs, RAG services, model management, and enterprise AI software into a unified solution. We are seeking a hands-on Private Cloud AI Platform Engineer to help build and enhance our on-premises AI platform. This role is focused on developing enterprise-grade software that allows customers to deploy and operate AI infrastructure within their own data centers, similar to how platforms such as Nutanix, VMware, Open Shift, and other private cloud solutions are delivered and managed. Role Overview As a Private Cloud AI Platform Engineer, you will work on the software that powers Qubrid's on-prem AI platform. You will develop features that simplify deployment, management, monitoring, and operation of AI infrastructure, GPU clusters, and AI models in enterprise environments. The ideal candidate enjoys building products that combine software engineering, cloud-native technologies, infrastructure automation, Linux systems, networking, and AI infrastructure. This is a hands-on engineering role requiring strong coding skills along with 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. Preferred Qualifications Experience developing private cloud, virtualization, or enterprise infrastructure platforms. Experience with technologies such as VMware, Nutanix, Open Shift, Rancher, Kube Virt, or Open Stack. Experience with GPU infrastructure and AI workloads. Knowledge of enterprise authentication systems such as LDAP, Active Directory, SAML, or OAuth. Experience with infrastructure automation tools. Familiarity with AI model deployment and inference platforms. Experience with monitoring tools such as Prometheus and Grafana. Technical Skills Software Development Python REST APIs Microservices Postgre SQL Redis Infrastructure Linux Kubernetes Docker Networking Storage Systems Virtualization Platforms Enterprise Technologies Active Directory LDAP SAML OAuth RBAC Audit Logging Monitoring & Operations Prometheus Grafana Logging and Observability System Monitoring Troubleshooting and Root Cause Analysis What You'll Build Enterprise .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.