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Connected Logistics

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DevSecOps Engineer - VA

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Connected Logistics is seeking a DevSecOps Engineer to join a platform engineering team supporting the Department of Veterans Affairs Chief AI Office (CAIO), building and operating the cloud infrastructure, CI/CD pipelines, and containerization workflows that support AI/ML and software delivery for pilot programs. The platform runs on Azure Kubernetes Service (AKS), automated via GitHub Actions reusable workflows, with infrastructure-as-code being stood up in Pulumi (TypeScript). You'll own key parts of the lifecycle — from reusable workflow libraries and container build/scan pipelines to multi-tenant AKS platform operations and federal security compliance.

What You'll Get to Do as a DevSecOps Engineer

  • Author and maintain a reusable GitHub Actions workflow library consumed by tenant application repositories to build, scan, sign, and deploy.
  • Build and execute CI/CD pipelines that deploy a self-service platform into customer Azure environments; help establish and expand Pulumi (TypeScript) IaC patterns from the current baseline.
  • Deploy and maintain cloud platform infrastructure: AKS clusters, load balancers, node pools, NSGs, and Istio service mesh routing.
  • Containerize tenant applications using multi-stage Docker builds and deploy them to AKS through automated pipelines.
  • Integrate and maintain a security scanning pipeline using Semgrep (SAST), Grype/Snyk (SCA), and Trivy (container image scanning), plus SBOM generation and image signing (Cosign/Sigstore), enforcing quality gates in all CI workflows.
  • Automate multi-tenant platform onboarding: namespace provisioning, RBAC role bindings, and virtual service creation via scripted workflows.
  • Manage container image registries (GHCR or equivalent), versioning, and release automation (e.g., Release Please, Dependabot).
  • Help stand up and maintain observability infrastructure for platform and AI/ML workloads.
  • Support the AI/ML platform stack (models running on Kubernetes; Azure AI Foundry and Azure ML Workspaces as the platform matures).
  • Collaborate with software engineers to containerize their applications for production deployment.
  • Follow DevSecOps best practices for security integration, IaC, SRE, and operational visibility.
  • Produce lightweight but useful documentation of systems, infrastructure, and automation mechanisms.

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