Principal Core Infrastructure Engineer

Oracle Oracle · Enterprise · India

Principal Core Infrastructure Engineer focused on designing, implementing, and maintaining infrastructure for customer AI/ML initiatives, including pre/post sales support, optimization, and troubleshooting of AI/ML workloads on Oracle Cloud Infrastructure (OCI).

What you'd actually do

  1. Design, deploy, and manage infrastructure components such as cloud resources, distributed computing systems, and data storage solutions to support AI/ML workflows.
  2. Collaborate with scientists and software/infrastructure engineers to understand infrastructure requirements for training, testing, and deploying machine learning models.
  3. Implement automation solutions for provisioning, configuring, and monitoring AI/ML infrastructure to streamline operations and enhance productivity.
  4. Optimize infrastructure performance by tuning parameters, optimizing resource utilization, and implementing caching and data pre-processing techniques.
  5. Ensure security and compliance standards are met throughout the AI/ML infrastructure stack, including data encryption, access control, and vulnerability management.

Skills

Required

  • scripting and automation using tools like Ansible, Terraform, Python and/or Kubernetes
  • containerization technologies (e.g., Docker, Kubernetes)
  • orchestration tools (like Slurm, PBS, etc.)
  • networking concepts, security principles, and best practices
  • problem-solving skills
  • communication and collaboration skills
  • documentation skills
  • Linux skills with hands-on experience in Oracle Linux/RHEL/CentOS, Ubuntu, and Debian distributions

Nice to have

  • Python, Rust, Go, Java, or Scala
  • designing, implementing, and managing infrastructure for AI/ML or HPC workloads
  • machine learning frameworks and libraries such as TensorFlow, PyTorch, or sci-kit-learn
  • DevOps practices and tools
  • High-Performance Computing/GPU systems

What the JD emphasized

  • AI/ML Forward Deployed Infrastructure Engineer
  • customers AI and machine learning initiatives
  • AI/ML solutions
  • AI/ML infrastructure
  • AI/ML or HPC workloads

Other signals

  • customer-facing
  • infrastructure
  • AI/ML workloads
  • GPU
  • performance optimization