Senior Lead SysOps/Devops Engineer Integrant Inc

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