Nvidia Senior HPC AI Cluster Engineer 

United Kingdom, England, Southampton 

906117010

Designing, implementing and maintaining large scale HPC/AI clusters with monitoring, logging and alerting

Managing Linux job/workload schedules and orchestration tools

Developing and maintaining continuous integration and delivery pipelines

Developing tooling to automate deployment and management of large-scale infrastructure environments, to automate operational monitoring and alerting, and to enable self-service consumption of resources

Deploying monitoring solutions for the servers, network and storage

Troubleshooting and fixing, bottom up from bare metal, operating system, software stack and application level

Being a technical resource, developing, re-defining and documenting standard methodologies to share with internal teams

Supporting Research & Development activities and engaging in POCs/POVs for future improvements

Bachelor's Degree in Computer Science, Engineering, or a related field; or equivalent experience

5+ years of experience

Knowledge of HPC and AI solution technologies from CPU’s and GPU’s to high speed interconnects and supporting software

Experience with job scheduling workloads and orchestration tools such as Slurm, K8s

Excellent knowledge of Windows and Linux (Redhat/CentOS and Ubuntu) networking (sockets, firewalls, iptables, wireshark, etc.) and internals, ACLs and OS level security protection and common protocols e.g. TCP, DHCP, DNS, etc.

Experience with multiple storage solutions such as Lustre, GPFS, zfs and xfs. Familiarity with newer and emerging storage technologies.

Python programming and bash scripting experience.

Comfortable with automation and configuration management tools such as Jenkins, Ansible, Puppet/chef

Deep knowledge of Networking Protocols like InfiniBand, Ethernet

Deep understanding and experience with virtual systems (for example VMware, Hyper-V, KVM, or Citrix)

Familiarity with cloud computing platforms (e.g. AWS, Azure, Google Cloud)

Knowledge of CPU and/or GPU architecture

Knowledge of Kubernetes, container related microservice technologies

Experience with GPU-focused hardware/software (DGX, Cuda)

Background with RDMA (InfiniBand or RoCE) fabrics