# Senior Lead SysOps/Devops Engineer > Integrant · Cairo, Egypt (Hybrid) · — · Posted 2026-04-26 **Workplace:** hybrid **Department:** Software Development ## Description We are seeking an exceptional Senior Lead who combines deep hands-on SysOps/HPC expertise with the strategic vision of a solution architect. This is a rare dual-track role: you operate at the intersection of elite technical execution and client-facing presales, designing and running mission-critical GPU, HPC, and Kubernetes platforms while simultaneously co-creating opportunity with our commercial teams. This role carries both SysOps, HPC depth and DevOps. You are expected to spend **at least 60% of your time on implementation and technical execution** ### What You Will Do **Presales & Business Development** •       Partner with sales and solution teams to identify and qualify new opportunities •       Lead or support technical presales activities: discovery workshops, RFP responses, architecture presentations •       Build and deliver proof-of-concepts (POCs) that demonstrate platform capabilities to prospective clients •       Prepare high-quality technical materials •       Act as a trusted technical advisor during client conversations, proposing solutions aligned to business goals **In-Account Delivery — SysOps & DevOps Execution** •       Operate directly within client accounts as a senior SysOps/DevOps engineer •       Run, troubleshoot, and optimize production-grade Kubernetes clusters and GPU/HPC environments hands-on •       Own Linux system administration at a deep level: kernel tuning, storage, networking, performance profiling •       Implement and maintain IaC pipelines, GitOps workflows, and CI/CD systems •       Serve as the senior escalation point for complex operational incidents within accounts **Architecture & Solution Design** •       Design end-to-end platform architectures spanning cloud, hybrid, and on-premises HPC environments •       Define workload isolation models, networking architectures, and storage strategies for multi-tenant platforms •       Recommend and validate technology choices aligned to client scale, budget, and team maturity •       Produce architecture decision records (ADRs), solution blueprints, and technical runbooks ### Technical Competencies & Requirements **1\. Architecture & System Design** •       Design production-grade multi-cluster Kubernetes platforms: ◦       RKE2, EKS (AWS), AKS (Azure) at enterprise scale ◦       GPU-aware clusters: NVIDIA H100 / A100 / B200 node pools ◦       Hybrid cloud + on-premises HPC infrastructure •       Define and document: ◦       Workload isolation: namespaces, MIG partitioning, multi-tenancy models ◦       Networking: BGP peering, Ingress controllers, service mesh (Istio / Cilium) ◦       Storage: Longhorn, Ceph, distributed and high-throughput file systems **2\. Platform Engineering & GitOps Strategy** •       Define and enforce platform standards across the delivery lifecycle •       GitOps tooling: ArgoCD, Fleet — declarative cluster management •       CI/CD pipelines: Azure DevOps, Jenkins — build, test, promote •       Infrastructure as Code: Terraform (modules, remote state, workspaces), Ansible •       Standardize cluster bootstrapping, app deployment lifecycle, environment promotion (Dev → QA → Prod) **3\. AI / GPU Infrastructure Architecture  (Priority Competency)** •       Design and operate GPU compute platforms at scale: ◦       GPU Operator deployment and lifecycle management ◦       MIG (Multi-Instance GPU) partitioning for multi-tenant workloads ◦       Advanced scheduling: Run:AI, Kubernetes-native GPU scheduling (device plugins) •       Understand AI workload classes and their infrastructure implications: ◦       Distributed training workloads (data/model/pipeline parallelism) ◦       Inference pipelines — NVIDIA Triton Inference Server, TensorRT optimization •       Align infrastructure to the full AI stack: ◦       CUDA stack, cuDNN, NCCL collective communication libraries ◦       High-speed networking: InfiniBand (HDR/NDR), RoCE for RDMA ◦       GPUDirect RDMA / GPUDirect Storage for low-latency data paths **4\. Observability & Reliability Engineering** •       Define and implement full-stack observability: ◦       Metrics: Prometheus, Thanos (long-term retention, multi-cluster) ◦       Logs: Loki, Fluent Bit ◦       GPU telemetry: DCGM Exporter, NVIDIA Nsight Systems •       Build operational frameworks: ◦       SLO / SLA definitions and error budget tracking ◦       Alerting strategy — noise reduction, severity routing ◦       Incident response playbooks and on-call runbooks **5\. Security & Multi-Tenancy Architecture** •       Design zero-trust security postures for multi-tenant platforms •       Secret management: HashiCorp Vault, External Secrets Operator •       Identity and access: IAM, RBAC, SSO/OIDC integration •       Network isolation: NetworkPolicy, micro-segmentation, mTLS •       Secure GPU sharing: MIG isolation, VGPU licensing, tenant boundary enforcement **6\. HPC, Data & Storage Architecture  (Priority Competency)** •       Understand the high-performance storage for AI/HPC workloads: ◦       GPUDirect Storage — bypassing CPU for GPU-native I/O ◦       Distributed file systems: Weka (high-throughput NFS/S3), Ceph (scalable object/block) ◦       Storage tiering, caching strategies, and data lifecycle management •       Size and validate storage architectures against workload I/O profiles **7\. Operational Leadership & Linux Systems** •       Lead incident response and root cause analysis (RCA) for critical production issues •       Define upgrade strategies, change management procedures, and disaster recovery plans •       Write and maintain runbooks, operational playbooks, and knowledge base content •       Integrate organizational processes, compliance requirements, and security policies into operational frameworks •       Deep Linux expertise: ◦       Kernel tuning (CPU governor, NUMA, IRQ affinity, hugepages) ◦       Storage I/O scheduling, NVMe optimization ◦       Network stack tuning for RDMA / InfiniBand ◦       System performance profiling and bottleneck analysis ### Candidate Profile — Who You Are •       you are comfortable running production systems. •       You have stronger SysOps and HPC depth than DevOps breadth, and you embrace that identity •       You can shift fluidly between running a live incident, presenting an architecture to a CTO, and reviewing a POC demo environment •       You communicate technical complexity clearly — to engineers and to C-level stakeholders •       You understand why specific tooling choices matter (not just how to configure them) and can articulate trade-offs in presales conversations •       You are comfortable owning outcomes across both commercial (presales) and delivery (operations) dimensions •       You thrive in ambiguity and can scope both short POCs and long-horizon platform programs ## Requirements **Required** •       10+ years in platform/infrastructure engineering, with at least 2 years in architect-level role •       Proven hands-on experience operating Kubernetes at scale in production (multi-cluster, multi-tenant) •       Significant Linux systems administration experience — kernel, networking, storage at a low level •       HPC and/or GPU infrastructure experience — physical GPU servers, NCCL, InfiniBand, or high-speed fabrics •       Demonstrable presales or client-facing experience •       IaC experience: Terraform and/or Ansible in production environments •       Strong understanding of GitOps and CI/CD pipelines in enterprise settings **Strongly Preferred** •       Experience with NVIDIA GPU Operator, MIG partitioning, Run:AI, or equivalent GPU scheduling tooling •       Knowledge of distributed AI training infrastructure (PyTorch DDP, Horovod, DeepSpeed) from an infrastructure perspective •       Familiarity with NVIDIA Triton Inference Server or TensorRT deployment pipelines •       Experience with Weka, Ceph, or GPUDirect Storage in HPC/AI environments •       Hands-on experience with Vault, External Secrets, and zero-trust network architectures •       Exposure to bare-metal provisioning and HPC cluster management (Slurm, PBS, or equivalent) **Certifications (Advantageous)** •       CKA / CKS (Certified Kubernetes Administrator / Security Specialist) •       RHCE / RHCA (Red Hat Certified Engineer / Architect) •       AWS Solutions Architect / Azure Solutions Architect Expert •       HashiCorp Terraform Associate or Vault Associate •       NVIDIA DLI certifications (GPU computing, AI infrastructure) ## Apply [Apply at Integrant](https://apply.workable.com/integrant/j/C7A37A43E9/apply) --- Powered by [Workable](https://www.workable.com)