Job 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 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)
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