AI/ML Infrastructure Engineer

We are at the forefront of developing cutting-edge AI solutions that push the boundaries of machine learning, LLM applications, and agentic AI. Our team builds real-world AI systems and deploys scalable, production-ready solutions across Oracle's enterprise customers.

We are seeking a highly skilled AI/ML Infrastructure Engineer to design, build, and support the systems, tools, and cloud services that power large-scale machine learning and agent-based workflows. In this role, you will leverage your expertise in distributed systems, cloud-native platforms, and software engineering to enable applied scientists to train, evaluate, and deploy state-of-the-art AI models efficiently and reliably.

You will collaborate across infrastructure, platform, and applied science teams to deliver secure, high-performance, and scalable infrastructure that accelerates AI innovation.

Key Responsibilities

• Design, deploy, and operate infrastructure components—including cloud compute, distributed systems, and data storage—to support AI/ML model training, evaluation, and deployment.
• Build automation pipelines for provisioning, configuring, and monitoring AI/ML infrastructure using Terraform, Docker, Kubernetes, and related tools.
• Optimize resource utilization and performance through cluster tuning, caching, data preprocessing, and system-level performance enhancements.
• Troubleshoot and resolve complex issues in distributed computing environments, ensuring high availability, reliability, and scalability.
• Enforce strong security and compliance standards through access control, vulnerability management, and encryption best practices.
• Partner closely with applied scientists, platform engineers, and cloud infrastructure teams to gather requirements and deliver frictionless ML workflows.
• Produce clear and comprehensive documentation for infrastructure, APIs, designs, troubleshooting, and best practices.
• Stay current with emerging trends in AI infrastructure, agent frameworks, HPC systems, and cloud-native technologies; evaluate and integrate them where appropriate.

Required Qualifications

• 4–7 years of software engineering experience focused on backend systems, distributed systems, or AI/ML applications.
• Hands-on experience with Docker and deploying containerized applications in Kubernetes environments.
• Strong Linux administration skills, including shell scripting, package management, troubleshooting, and performance tuning.
• Proven experience designing or managing infrastructure for AI/ML or HPC workloads. HA, Failover, Cross-Region DR
• Expertise with at least one public cloud (OCI, AWS, Azure, GCP) and willingness to specialize in OCI.
• Excellent communication skills, particularly in distributed and asynchronous team environments.
• Demonstrated ability to own problems end-to-end and collaborate effectively with internal teams and external customers..
• Knowledge of DevOps and CI/CD pipelines.

Additional Skills

• Strong research and evaluation capabilities for new technologies and approaches.
• Advanced understanding of applications, server technologies, network routing, and security.