Infrastructure & Data Science Lead - Locals Preferred

Rootshell Enterprise Technologies•Pleasanton, CA
•Onsite

About The Position

Rootshell Enterprise Technologies Inc. is seeking an experienced Infrastructure & Data Science Lead for a client. The role will design and operate scalable AI and data-science infrastructure for healthcare and research, lead cross-functional projects involving IT, security, and platform teams, and deliver production-grade machine-learning and large-language-model systems as well as researcher-facing platforms. The position is onsite in Pleasanton, California; locals are preferred.

Requirements

  • Advanced degree in computer science, data engineering, biomedical informatics, or a similar field.
  • At least 7 years of experience building and operating data-science and AI infrastructure with open-source tools.
  • Proven leadership of multi-team platform projects involving IT, security/compliance, and platform owners.
  • Hands-on production Kubernetes and container experience.
  • Experience with healthcare/research machine-learning and large-language-model applications.
  • Strong R and Python analytics skills.
  • Python and Linux/Unix scripting skills for automation.
  • Experience with distributed, scalable machine-learning systems and CI/CD in research or machine-learning contexts.
  • Excellent communication skills and ability to translate researcher and clinical needs into platform features.

Nice To Haves

  • Experience in clinical research, public health, or life sciences.
  • Familiarity with HIPAA/IRB and regulated data environments.
  • Experience with multi-institution research collaborations and on-premises/private-cloud machine-learning deployments.

Responsibilities

  • Design and operate scalable AI and data-science infrastructure for healthcare and research.
  • Lead cross-functional platform projects involving IT, security/compliance, and platform owners.
  • Deliver production-grade machine-learning and large-language-model systems and researcher-facing platforms.
  • Translate researcher and clinical needs into platform features.
  • Use Python and Linux/Unix scripting to automate infrastructure and workflows.
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