AI/ML Platform Engineer: Kubernetes & GitOps

BullTechSystem

Halifax

On-site

CAD 90,000 - 130,000

Full time

3 days ago
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Job summary

BullTechSystem is seeking a Platform Engineer to deploy and manage our AI/ML infrastructure, including LlamaIndex Cloud and KDB.AI on Kubernetes.

You will build reliable, scalable deployment pipelines using GitOps (Flux CD), administer multiple Kubernetes environments, secure secrets with Vault, and collaborate with the identity, CI/CD, and platform teams to keep workloads healthy and observable.

Qualifications

  • 3+ years managing production Kubernetes clusters.
  • 2+ years with Flux CD, ArgoCD, or similar GitOps tools.
  • Advanced Helm chart development and management.
  • HashiCorp Vault for secrets management.
  • Artifactory or similar container registries.
  • CI/CD with GitHub Actions, Jenkins, or similar.

Responsibilities

  • Deploy and manage LlamaIndex Cloud and KDB.AI applications across environments.
  • Implement GitOps workflows using Flux CD for automated deployments.
  • Administer Kubernetes clusters across multiple environments.
  • Configure HashiCorp Vault and ExternalSecrets for secrets.
  • Maintain CI/CD pipelines with GitHub Actions and Artifactory.
  • Work with Identity Management to configure OIDC with Microsoft Entra ID.
  • Create and maintain Helm charts and Kubernetes manifests.
  • Monitor performance and troubleshoot production issues.
  • Document procedures, runbooks, and infrastructure patterns.

Skills

Kubernetes
GitOps
Helm
CI/CD
GitHub Actions
Jenkins
Vault
Container Registries

Tools

Flux CD
ArgoCD
HashiCorp Vault
Artifactory
PostgreSQL
MongoDB
Redis
PgBouncer
HAProxy

Job description

BullTechSystem is seeking a Platform Engineer to deploy and manage our AI/ML infrastructure, including LlamaIndex Cloud and KDB.AI on Kubernetes.

You will build reliable, scalable deployment pipelines using GitOps (Flux CD), administer multiple Kubernetes environments, secure secrets with Vault, and collaborate with the identity, CI/CD, and platform teams to keep workloads healthy and observable.

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