Results for “kubernetes-jobs”

Runs batch processing and high-performance computing (HPC) workloads on Google Kubernetes Engine (GKE), including job queues, parallel processing, and MPI workloads.

Build and audit Kubernetes operators with tools that validate CRD design, lint reconcile loops, and score OperatorHub capability levels.

Audit Kubernetes cluster security posture against CIS benchmarks using kube-bench with automated checks for control plane, worker nodes, and RBAC.

Create and manage Kubernetes workloads with declarative YAML manifests, covering deployments, networking, security, storage, Helm charts, and troubleshooting.

Provides production-grade Kubernetes YAML patterns and kubectl debugging commands for deploying, managing, and debugging workloads reliably.

Find over-permissive RBAC roles and service-account token abuse paths in Kubernetes using kubectl auth can-i, rbac-police, kubectl-who-can, and rakkess during authorized cluster security reviews.

Hardens Google Kubernetes Engine (GKE) clusters with Workload Identity, Secret Manager, RBAC, Binary Authorization, Network Policies, and Pod Security Standards.

Routes to specialized GKE sub-skills for cluster management, networking, security, scaling, and more on Google Kubernetes Engine.

Protects stateful GKE workloads by configuring backup plans, restore workflows, and disaster recovery using Backup for GKE.

Systematically evaluates Kubernetes cluster security by simulating attacker techniques against the API server, kubelet, etcd, pods, RBAC, network policies, and secrets using tools like kube-hunter, Kubescape, and kube-bench.

Generates production-grade Kubernetes manifests including Deployments with probes and security contexts, Services, Ingress with TLS, HPA, PDB, NetworkPolicy, ConfigMaps, and Secrets, with optional Helm charts, Kustomize overlays, Istio mesh, and ArgoCD GitOps.

Run CIS Kubernetes Benchmark checks and remediate findings with kube-bench.

Hardens managed Kubernetes clusters on EKS, AKS, and GKE by implementing Pod Security Standards, network policies, workload identity, RBAC scoping, image admission controls, and runtime security monitoring.

Runs kubectl and helm commands with kubeconfig injection, emphasizing parallel execution, JSON output with jq filtering, and batch operations for efficient cluster management.

Deploys and manages Google Cloud Platform backends for Flutter apps, covering GKE clusters, Cloud Run services, Cloud SQL databases, Cloud Storage buckets, and Firebase integration.

Diagnoses and operates Kubernetes clusters: pods, deployments, ingress, HPA, PVC, StorageClass, NetworkPolicy, Secrets, Helm, ArgoCD, cert-manager. Reads before changing; mutations require confirmation and diff review.

Set up and manage Google Kubernetes Engine clusters, node pools, workloads, networking, and storage with Autopilot defaults.

Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters, producing upgrade plans, checklists, and runbooks with gcloud commands.

Plans and configures multi-tenancy on GKE, covering namespace isolation, RBAC planning, resource quotas, LimitRanges, network isolation, and cost allocation.

Authorized security assessment for cloud, container, and Kubernetes environments covering metadata SSRF, IAM misconfigurations, container escape paths, and cluster RBAC review.

Manages Google Kubernetes Engine clusters via gcloud CLI, covering cluster discovery, node pool management, workload analysis, autopilot configuration, and upgrade planning.

Manages Helm charts, releases, repositories, and template rendering for Kubernetes, including installation, upgrades, rollbacks, and debugging.

Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates for deployment automation, GitOps, and incident response.

Implements Kubernetes network policies for micro-segmentation, covering ingress/egress rules, Calico and Cilium, with a Python CLI for managing and validating policies.

Deploy and manage a Linkerd service mesh on Kubernetes, including mTLS, traffic splitting, retries, timeouts, and observability.

Manage Kind (Kubernetes in Docker) clusters for local development and testing, including cluster creation, platform deployment, and E2E test workflows.

Submit and monitor GPU training jobs on Brev, SLURM, Docker, or Kubernetes using the TAO Execution SDK, with job handles, S3 I/O wrapping, and multi-node distributed training.