DevOps Support Engineer

About AI Factory

The AI Factory operates sovereign AI infrastructure including:

• GPU clusters

• Cloud subscriptions

• Containerized workloads

• API gateways

• Multi-environment deployments (Sandbox → Staging → Production)

The DevOps Support Engineer ensures:

• Infrastructure stability

• Deployment reliability

• Operational continuity for AI workloads

Role Overview

Employment Type: Full-time

Work Arrangement: Onsite (Applicants based outside the UAE are required to relocate)

The DevOps Support Engineer is responsible for supporting:

• Cloud infrastructure

• CI/CD pipelines

• Containerized AI workloads

• API gateways

• Production environments

The role focuses on:

• Platform stability

• Environment health

• Deployment reliability

• Infrastructure troubleshooting

• Structured incident management

• Environment discipline

• Production governance

This is an operational reliability role aligned with modern DevOps, SRE, and AIOps practices.

The engineer acts as:

• L1 operational responder for infrastructure/platform incidents

• Ensures issues are diagnosed, contained, escalated appropriately

• Ensures resolution within defined service levels

Key Responsibilities

1. Infrastructure, Cloud & Environment Support

• Support Azure subscriptions, resource groups, networking, and access control

• Monitor GPU environments, container clusters, and AI runtime environments

• Troubleshoot deployment failures across Sandbox, Staging, and Production

2. DevOps & CI/CD Support

• Monitor CI/CD pipelines and resolve build/deployment issues

• Support Git workflows, version control issues, and release rollouts

• Ensure environment configuration consistency

• Validate infrastructure changes post-deployment

• Perform rollback support when required

3. GPU & AI Runtime Operations Support

• Monitor GPU utilization and allocation

• Identify memory saturation and CUDA/container runtime errors

• Support AI model deployment on GPU nodes

• Detect performance bottlenecks affecting inference services

4. API Gateway, WAF & Integrations

• Troubleshoot API gateway routing issues and throttling policies

• Monitor rate limiting and traffic control mechanisms

• Investigate WAF-related blocking incidents

• Support secure external integrations

• Support integrations with enterprise systems:

  • Microsoft 365

  • SharePoint

  • Teams

  • Oracle

  • Jira

• Troubleshoot authentication issues, webhook failures, and API timeouts

5. Observability & Incident Response

• Monitor service availability, CPU/GPU utilization, memory, storage, and logs

• Detect infrastructure bottlenecks affecting AI workloads

• Act as first-line responder for infrastructure and platform-related incidents (P0–P3)

• Perform triage using logs, metrics, system databases, and environment diagnostics

• Classify incidents by severity and business impact in line with defined SLAs

• Contain and mitigate production-impacting issues

• Coordinate with L2/L3 teams and vendors

• Escalate with full diagnostic context (logs, metrics snapshots, timestamps, components)

• Track incident lifecycle to closure and ensure no SLA breach

6. Documentation & Knowledge Management

• Maintain and improve:

  • Infrastructure runbooks

  • Deployment troubleshooting guides

  • Environment configuration documentation

  • FAQs

• Document recurring failure patterns (deployment errors, GPU saturation, network misconfigurations)

• Handle ITSM/ticketing documentation

• Capture and publish Root Cause Analysis (RCA) summaries for major incidents

• Update environment diagrams and operational checklists after changes

7. Platform Reliability

• Support Kubernetes clusters, Docker containers, and orchestration layers

• Validate scaling, failover, and resilience mechanisms

• Ensure uptime SLAs for AI products, platforms, and APIs

8. Security & Compliance Coordination

• Support IAM, access control, WAF, and network configurations

• Coordinate with security teams for incident remediation

• Ensure adherence to environment governance policies

Required Technical Skills

• Strong hands-on experience with Azure (AWS/GCP acceptable)

• Experience supporting Kubernetes and Docker environments

• Familiarity with CI/CD tools (Azure DevOps, GitHub Actions, Jenkins)

• Experience with monitoring tools (Azure Monitor, Dynatrace, Grafana)

• Understanding of networking, IAM, API gateways, and WAF

• Experience supporting production cloud environments under SLA constraints

• Familiarity with Infrastructure-as-Code concepts (ARM/Terraform)

Experience

• 4–7 years in DevOps, Cloud Operations, Platform Support, or SRE-aligned roles

• Experience supporting containerized or AI workloads preferred

• Exposure to regulated or government environments advantageous

• Arabic speaker is a plus