AI Infrastructure Specialist

7 days ago

Singapore beBeeArtificial Full time

Job Opportunity

We are seeking a highly skilled AI Platform Developer to join our software engineering team. The successful candidate will be responsible for designing, developing, and operating core components of our next-generation AI computing platform.

About the Role

This is an exciting opportunity for a talented developer to make a significant impact on our software development efforts. As a key member of our team, you will work with cross-functional groups to develop and deploy cutting-edge tools and infrastructure that enable application teams to innovate.

Main Responsibilities

  • Design, develop, and operate core components of the AI platform, including MLOps pipelines, data handling, and model serving infrastructure.
  • Implement and manage Large Language Model (LLM) tooling and prompt engineering frameworks to streamline development and enhance team productivity.
  • Deploy and maintain scalable compute environments on platforms like OpenShift and VxRail , ensuring high availability and performance for AI workloads.
  • Collaborate with AI Application Developers to understand their needs and provide robust, easy-to-use tools and APIs.
  • Troubleshoot and resolve complex technical issues across the platform stack, from the infrastructure to the application layer.
  • Contribute to technical documentation and best practices for platform usage.

Requirements

  • Bachelor's or Master's degree in computer science or a related field with 5+ years of relevant experience.
  • Strong programming skills in Python, Go, or a similar language.
  • Hands-on experience with containerization and orchestration technologies (Kubernetes, OpenShift).
  • Experience building or managing infrastructure for machine learning or other high-performance computing workloads.
  • Familiarity with the LLM ecosystem, including frameworks like LangChain, and vector databases.

Priorities

  • Experience with VMware's technology stack (vSphere, Tanzu).
  • Knowledge of GPU management and scheduling in a containerized environment.

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