Senior Lead SysOps DevOps Engineer

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)