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Systems & Application Server Engineer

F-Technologies LLC
Dubai, UAE
Full-time
Senior · 5+ years experience
Onsite
Discovered 3 weeks ago
GitHub EnterpriseTFSAzure DevOps ServerGitHub ActionsAzure DevOps PipelinesJenkins

Full Job Posting

  • Administer and support GitHub Enterprise, TFS, Azure DevOps Server, or similar platforms.
  • Manage repositories, permissions, branch protection, pull request rules, code review policies, and audit logs.
  • Manage self-hosted runners, build agents, service connections, tokens, secrets, and deployment credentials.
  • Maintain developer platform availability, access governance, and operational documentation.
  • Build, maintain, and improve CI/CD pipelines for VM-based and containerized applications.
  • Automate build, test, release, deployment, rollback, and environment setup processes.
  • Create deployment checklists, release gates, rollback plans, and approval processes.
  • Ensure all production deployments are auditable and traceable.
  • Build and maintain CI/CD workflows for Docker and containerized deployments.
  • Automate container image build, scan, tag, push, deploy, and rollback.
  • Support Kubernetes/OpenShift deployments; use Helm or similar for repeatable deployments.
  • Troubleshoot CrashLoopBackOff, image pull errors, failed probes, ingress issues, secrets/config issues, and deployment failures.
  • Own the deployment and operational management of GPU server infrastructure for AI workloads — driver management, CUDA/ROCm stack, GPU health monitoring, and resource scheduling.
  • Deploy and manage AI model serving infrastructure — vLLM, Triton Inference Server, Ollama, or similar — as containerized, monitored, and auditable workloads.
  • Own the AI API gateway — rate limiting, routing, authentication, quota enforcement, and cost tracking for LLM and model endpoints.
  • Manage vector database infrastructure (Qdrant, Weaviate, pgvector, or similar) and AI pipeline orchestration tools (Airflow, Prefect, or similar).
  • Automate GPU node provisioning, model deployment pipelines, and model version rollback.
  • Monitor AI workload performance — GPU utilization, inference latency, token throughput, and queue depth — and alert on degradation.
  • Coordinate with the Infrastructure Lead on GPU access governance, cost control, security posture, and capacity planning for AI workloads.
  • Build secure CI/CD pipelines with automated checks, approvals, logging, and rollback.
  • Enforce branch protection, pull request rules, code review requirements, and release gates.
  • Secure pipeline secrets, tokens, service connections, deployment keys, and build agents.
  • Implement secret scanning, dependency scanning, and vulnerability checks where possible.
  • Cross-train with the Systems & Application Server Engineer to provide mutual coverage during absences — able to check VM health, verify backup job status, restart services, and escalate appropriately.
  • Cross-train with the Infrastructure Lead to support monitoring dashboards, incident triage, and DR runbook execution.
  • Ensure all pipelines, automation scripts, and deployment procedures are fully documented so any team member can cover without depending on tribal knowledge.
  • 5+ years in DevOps, release engineering, infrastructure automation, platform engineering, or systems automation.
  • Strong experience with CI/CD tools (GitHub Actions, Azure DevOps Pipelines, Jenkins, GitLab CI, or similar).
  • Experience administering/supporting GitHub Enterprise, TFS, Azure DevOps Server, or similar source control platforms.
  • Strong Docker and container deployment experience; building Docker images and managing containerized pipelines.
  • Practical experience with Kubernetes, OpenShift, Helm, or similar orchestration platforms.
  • Strong scripting using PowerShell plus Bash or Python.
  • Experience managing build agents, runners, secrets, service connections, and pipeline permissions.
  • Must be based in Dubai, UAE.
  • Terraform, Ansible, Packer, or similar IaC tools.
  • VMware, Hyper-V, Proxmox, OpenStack, Nutanix, or other virtualization platforms.
  • Security/dependency/secret scanning and DevSecOps practices.
  • OpenShift, Kubernetes operators, GitOps, ArgoCD, or Flux.
  • GPU infrastructure — NVIDIA drivers, CUDA, GPU node pools in Kubernetes, NVIDIA device plugin, or bare-metal GPU server management.
  • AI model serving — vLLM, Triton Inference Server, Ollama, or similar; experience deploying and scaling LLM or ML model endpoints.
  • AI API gateway — Kong, Traefik, or custom gateway solutions for LLM routing, quota management, and cost tracking.
  • Vector databases — Qdrant, Weaviate, Chroma, Milvus, or pgvector; AI pipeline tools — Airflow, Prefect, or similar.
  • Strong preference for candidates with experience in regulated, policy-driven environments — semi-government entities, banking, telecom, energy, healthcare, or defense — where secure deployment practices, auditable pipelines, formal change approval, and documented release governance are enforced as standard.

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