Description
As a Principal Core Infrastructure Engineer (AI/ML Forward Deployed Infrastructure Engineer), you will play a critical role in designing, implementing, and maintaining the infrastructure that supports our customers' AI and machine learning initiatives.
You will work closely with customer technical teams (data scientists, software engineers, and Infra/IT professionals) and internal cloud services teams to ensure their AI/ML solutions are deployed efficiently, securely, and at scale.
Your expertise will be crucial in optimising our infrastructure for performance, reliability, and cost-effectiveness. In this hands-on technical role, you'll troubleshoot issues for proof of concept (POC) and production deployments.
Responsibilities
- Work with OCI Strategic customers to grow our business in pre/post sales stages in a technical infra expert role.
- Take ownership of problems and work to identify solutions.
- Ability to think through the solution and identify/document potential issues impacting your customers.
- Design, deploy, and manage infrastructure components such as cloud resources, distributed computing systems, and data storage solutions to support AI/ML workflows.
- Collaborate with scientists and software/infrastructure engineers to understand infrastructure requirements for training, testing, and deploying machine learning models.
- Implement automation solutions for provisioning, configuring, and monitoring AI/ML infrastructure to streamline operations and enhance productivity.
- Optimise infrastructure performance by tuning parameters, optimising resource utilisation, and implementing caching and data pre-processing techniques.
- Ensure security and compliance standards are met throughout the AI/ML infrastructure stack, including data encryption, access control, and vulnerability management.
- Troubleshoot infrastructure performance, scalability, and reliability issues and implement solutions to mitigate risks and minimise downtime.
- Stay updated on emerging technologies and best practices in AI/ML infrastructure and evaluate their potential impact on our systems and workflows.
- Document infrastructure designs, configurations, and procedures to facilitate knowledge sharing and ensure maintainability.
- As a trusted customer advocate, you will help customers/partners understand best practices around advanced GPU solutions, and how to migrate their workloads to the cloud.
- Educate customers of all sizes on the value proposition of Oracle Cloud and participate in deep architectural discussions to ensure solutions are designed for successful deployment in the cloud.
- Act as a technical liaison between customers, core engineering teams, and support.
Qualifications:
- Experience in scripting and automation using tools like Ansible, Terraform, Python and/or Kubernetes.
- Experience with containerisation technologies (e.g., Docker, Kubernetes) and orchestration tools (like Slurm, PBS, etc.) for managing distributed systems.
- Solid understanding of networking concepts, security principles, and best practices.
- Excellent problem-solving skills, with the ability to troubleshoot complex issues and drive resolution in a fast-paced environment.
- Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams and convey technical concepts to non-technical stakeholders.
- Strong documentation skills with experience documenting infrastructure designs, configurations, procedures, and troubleshooting steps to facilitate knowledge sharing, ensure maintainability, and enhance team collaboration.
- Strong Linux skills with hands-on experience in Oracle Linux/RHEL/CentOS, Ubuntu, and Debian distributions, including system administration, package management, shell scripting, and performance optimisation.
Preferred Qualifications
- Strong proficiency in at least one of the programming languages such as Python, Rust, Go, Java, or Scala.
- Proven experience designing, implementing, and managing infrastructure for AI/ML or HPC workloads.
- Understanding machine learning frameworks and libraries such as TensorFlow, PyTorch, or sci-kit-learn and their deployment in production environments is a plus.
- Familiarity with DevOps practices and tools for continuous integration, deployment, and monitoring (e.g., Jenkins, GitLab CI/CD, Prometheus).
- Strong experience with High-Performance Computing/GPU systems.