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)