Infrastructure & Data Center Architect/Lead providing technical leadership in data center operations for genomic workloads including HPC, Imaging, and AI-Large Language Models. Architecting enterprise solutions for optimized research environments.
Responsibilities
The Data Infrastructure Operations Lead for Genomic workloads will be the Infrastructure & Data Center Architect/Lead and will provide technical leadership and oversee / maintain data center operations for genomic workloads requiring HPC, Imaging, and AI-Large Language Models (LLM).
The candidate will be able to architect, design, develop and implement enterprise and cloud-based infrastructure and platform solutions.
As a technical leader, you will work on complex infrastructure problems spanning on-premises and cloud and be able to create innovative solutions to provide a seamless experience for researchers.
You will be able to lead, design and build data centers for next generation computing capabilities.
You will also be able to perform routine system administration tasks, troubleshooting high availability systems, log data, optimize performance, deploy GPU and implement automation techniques.
Requirements
Bachelor's with 15+ years (or commensurate experience)
Extensive Data Center architecture and design experience
Data Center rack and stack experience
Network design encompassing a hybrid ecosystem
Knowledge of Software Development Lifecycle and agile methodologies
Infrastructure Architecture experience extensively in on-premises, and preferably in cloud (AWS, Azure), hybrid cloud
Experience in architecting and designing GPU's, HPC clusters and pipelines for genomics processing and AI workloads.
Knowledge of virtualization techniques and working experience on hypervisors
MLOPs, DevSecOps experience is a plus.
Experience in secure and scalable infrastructure architecture for web applications, data systems and back-end applications
Knowledge of IaaS, PaaS components in on-premises (private cloud) and preferably in cloud (hybrid and public / gov)
Knowledge of infrastructure for distributed data systems
Knowledge of infrastructure for AI workloads including analysis pipelines and neural network models for genomics workloads
Knowledge of GPU virtualization, GPU partitioning, multi-GPU in bare-metal and virtualized env is a plus.
In-depth knowledge of network design, server and storage design in a highly scalable, fault-tolerant model is a must.
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