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2024 Infrastructure DevOps Developer Intern



Software Engineering, Other Engineering
Washington, DC, USA · Durham, NC, USA · Raleigh, NC, USA · Durham, NC, USA · Chapel Hill, NC, USA
Posted on Tuesday, January 9, 2024
At IBM, work is more than a job – it’s a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you’ve never thought possible. Are you ready to lead in this new era of technology and solve some of the world’s most challenging problems? If so, lets talk.

Your Role and Responsibilities
The IBM Technology Lifecycle Services (TLS) group is looking for technically oriented, talented, innovative, and enthusiastic interns in the role of a DevOps Developer / Engineer who would like to gain experience in modern AI technologies and help us adopt the technology as part of our AI4Infrastructure initiative for various use cases that shall help us increase productivity for our support engineers, reduce time-to- resolution for client cases and increase support deflection rates. The priority for IBM is to scale Watsonx as the AI for the business platform for enterprise clients and establish IBM as the top-of-mind choice for AI and foundation models.

Our Watsonx suite makes it possible for clients to build, train, tune, and deploy AI across their business, leveraging critical, trusted data wherever it resides. TLS is aiming to become client zero in capturing the full potential of this platform.

Required Technical and Professional Expertise

  • Good written and verbal communication skills and a strong programming or engineering background
  • Pursuing a STEM or Information Technology related bachelor’s degree

Preferred Technical and Professional Expertise

  • DevOps Developer or Engineer role or similar
  • Scripting and Python programming language
  • Analytics and interactive visualization platforms like Grafana
  • Continuous Integration/Continuous Deployment (CI/CD) pipelines
  • Tekton
  • Terraform modules
  • Cloud-based services
  • Machine Learning (ML) Ops and maintenance
  • Containerization technologies (e.g. Docker, Kubernetes, RedHat OCP).
  • Monitoring and operating Kubernetes clusters
  • Networking and security concepts
  • Problem-solving and communication skills