AWS Cloud Engineer

Job SummaryWe are looking for a Senior Engineer to join our Cloud Development Team and help develop and deploy services. Working closely with our colleagues in Platform Engineering, Datacentre Operations and Product Development teams, you will deploy services on our fleet of powerful AI systems. As part of our Platform Engineering organisation, you will be involved in the cloud integration, validation, performance benchmarking, optimisation, and development of our high-performance AI solutions. These include in-house AI systems alongside off-the-shelf high-performance servers, switches and storage solutions. This is a hand-on role requiring a solid background in use of cloud infrastructure, deployment using Infrastructure-as-Code, observability, high-performance networking and storage systems. You may have been working in an IT organisation, a datacentre, a cloud provider or as a developer of orchestration or cloud services.

The Platform Engineering Team at GraphcoreWe build Graphcore products into large-scale AI solutions for our customers and the Cloud Development Team is responsible for providing such systems to both internal users via private clouds and customers via our own public clouds. Often the internal systems will be using and developing pre-release hardware and software, so it's vital you are comfortable with unproven components.

Responsibilities and Duties

Develop and operate end-user services on our private clouds and support internal users in their use. You will turn end-user and product requirements into deployed services.

Help to build automation to collect and analyse metrics and other observability data from the cloud services to support clear identification and reporting of any issues. Work with users to provide information of any product-related issues to Engineering and QA departments.

Work with our Datacentre Operations Engineers to maintain and operate the fleet of AI systems at peak performance in our private clouds.

Operate and extend existing OpenStack-based cloud services and contribute to the deployment and development of new ones.

Configure and test new Graphcore AI hardware and systems using Continuous Deployment and Infrastructure-as-code in internal and external datacentres.

Drive corrective actions for systems that are not operating correctly, working with DC operations and Graphcore Engineering as required.

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Bachelor's degree or equivalent practical experience in a relevant subject.

Solid infrastructure or IT experience with a consistent track record of delivering technical output as an individual contributor.

Experience specifying, scoping, estimating and detailing work plans in an AGILE and SCRUM framework, including priorities, risks, issues, impacts and constraints.

Strong Linux scripting ability (bash and python required).

Strong Linux system administration (Ubuntu, RHEL and variants).

Experience with a version control system (preferably Git) and using it to manage system configuration or automation.

Experience with Continuous Integration or testing pipelines using GitLab, GitHub or similar.

Hands-on experience deploying services into public or private clouds using Infrastructure-as-Code (IAC).

A solid understanding of the technologies underpinning cloud services (APIs, virtualisation of CPUs, IO, systems), virtual networks, block storage, resource management and monitoring.

Experience with IAC automation tools (e.g. Terraform/OpenTofu, Ansible, Packer).

Experience with container deployment and management tools (e.g. docker).

Experience with solutions for monitoring and observability. e.g. Grafana, Prometheus, OpenSearch/ElasticSearch, Loki, Mimir, OpenTelemetry, Fluentd ,Kafka

Good communication and presentation skills, and experience dealing with end-users of IT or cloud services.

An ability to work independently on critical infrastructure without oversight, and with a focus on end-user availability.

Desirable but not required:

Experience with OpenStack deployments or the technologies they rely on (e.g. Ceph, Open vSwitch, KVM, QEMU ).

Strong skills and experience in HPC networking and virtualised RDMA NICs.

Strong skills and experience in end-to-end deployment automation and CI of containerised services. Complete automation of pipelines for build, test, deploy, manage, alert, destroy, rebuild.

Experience with managing production Kubernetes clusters and workloads.

Experience with workload queue management systems (SLURM, LSF, Kueue).

Experience with managed switch configuration (e.g. EOS, SONiC, DNOS).

Programming experience with Python3 utilising classes and inheritance.

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