Devops & SysOps Architect

About this role

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
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