Senior Infrastructure and Devops Engineer

Intel Intel · Semiconductors · Oregon, Hillsboro, United States +3

This role focuses on designing, deploying, and maintaining Linux-based infrastructure for large-scale modeling, simulation, and data analysis workflows within Intel's Silicon Architecture group. The engineer will manage CI/CD pipelines, automation, artifact storage, and monitor/improve pipeline performance and observability, working cross-functionally to enhance developer productivity and system scalability.

What you'd actually do

  1. Design, deploy, and maintain Linux-based infrastructure supporting architecture modeling, simulation, and projection workflows.
  2. Design and operate CI/CD pipelines for modeling frameworks, analysis tools, and supporting software components.
  3. Develop and maintain automation for build systems, toolchains, packaging, and dependency management.
  4. Enable scalable and reproducible execution of compute-intensive workloads.
  5. Manage artifact storage, versioning, and distribution for software builds and modeling outputs.

Skills

Required

  • Bachelor's Degree with 4+ or more years of experience, or a Master's Degree with 3+ years of experience, or a PhD in Computer Science, Information Systems, Electrical Engineering, Mathematics or any other STEM-related discipline.
  • 3+ years of experience in infrastructure engineering, DevOps, software engineering and build/release engineering.
  • 3+ years of experience working in Linux development environments.
  • 2+ years of experience with CI/CD systems (Jenkins, GitHub Actions, GitLab CI, or similar).
  • 2+ years of experience with build systems such as GNU Make, CMake, Bazel, or Ninja.

Nice to have

  • Advanced STEM degree and 7+ years of professional experience.
  • Experience writing scripts or software for automation (Python, Bash, or similar).
  • Experience supporting software development workflows such as build systems, testing pipelines, or deployment processes.
  • Experience with container technologies such as Docker or Kubernetes.
  • Experience supporting large-scale compute environments, HPC systems, or distributed workloads.
  • Experience with job scheduling systems (e.g., Slurm or similar).
  • Experience managing storage systems or distributed filesystems (e.g., NFS or similar).
  • Familiarity with performance tuning and resource utilization optimization in shared compute environments.
  • Experience supporting data analysis, modeling, or simulation workflows.
  • Familiarity with EDA tools, architecture modeling environments, or semiconductor development workflow