Senior Lead SysOps/Devops Engineer

Job Description Roles & Responsibilities 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 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 Desired Candidate Profile 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 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) Company Industry TelecomISP Department / Functional Area IT Software Keywords Senior Lead SysOps/Devops Engineer Get real-time job updates only on our App