Service Inventory Planning Systems Analyst at KLA developing planning parameters for demand forecasting and inventory optimization. Collaborating with teams to enhance data reporting and system usability.
About the role
- 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.
- Extensive knowledge of on-premises infrastructure and preferably cloud infrastructure (VPC, N/W, sub-nets, Cloud network connect, VPN, IAM, DB, K8, Autoscaling, Multi-zone, multi-region).
- Experience in design, create and build of IaaS and PaaS for on-premises environment.
- Knowledge of IaC (Infrastructure as Code), infra-automation, monitoring and tools such as Terraform
Benefits
- Employee Assistance Program (EAP)
- Corporate Discounts
- Learning & Development platform, to include certification preparation content
- Training, Education and Certification Assistance*
- Referral Bonus Program
- Internal Mobility Program
- Pet Insurance
- Flexible Work Environment
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