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objectives into robust pipelines, automate job orchestration and data movement, and optimize end-to-end workflow performance on large-scale Linux-based HPC environments and hybrid cloud/HPC platforms. Job
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centered on Microsoft Azure and related AI cloud technologies with High Performance Computing (HPC) for on premise AI environments and Azure, AWS, and GCP for off premise environments. Major Duties
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we treat one another, work together, and measure success. Operations Handle daily operations, coordinating the delivery of IT support across cloud platform operations. Work with team members to craft
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and platforms via APIs Experience working with ticketing systems such as Service Now and Atlassian products Experience with Azure or other cloud technologies Experience supporting DOE facilities
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., ML Ops for geospatial data, real-time analytics, cloud/HPC hybrid, edge geospatial compute, neuromorphic, quantum), pilot new capabilities, foster innovation partnerships (academia, industry, federal
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(e.g., Ansible, PowerShell) and scripting languages. Strong understanding of CPU and GPU environments, network protocols, storage solutions, and cloud technologies. Knowledge of security best practices
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, poisoning, and evasion attacks. Design platforms for threat modeling in AI systems, enabling comprehensive vulnerability assessments tailored to diverse use cases, from cloud-hosted models to edge deployments
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in Python and C/C++; familiarity with Unix/Linux environments. Experience architecting data pipelines (edge to cloud), data quality assurance, metadata and time synchronization, and secure remote
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organizations, regulatory agencies, peer groups, and the research community. Strong knowledge of a wide range of IT services relevant to research support, including network infrastructure, cloud computing
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authorization standards, including SAML, OAuth 2.0, OpenID Connect (OIDC), and LDAP. Familiarity with managing user identities and group memberships in LDAP and Active Directory Understanding of cloud-native IAM