AI Infrastructure Engineer (Storage)
Огляд ролі від JobGrid
AI Infrastructure Engineer (Storage) at CommonAI C.I.C.: Cambridge, Велика Британія; На місці; Повна зайнятість; IT; DevOps / SRE. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Cambridge, Велика Британія, На місці
- Role classification: IT, DevOps / SRE, Повна зайнятість
- Source freshness: checked by JobGrid on 2026-05-29.
- Application path: candidates continue to the employer application page with non-personal referral tags.
CommonAI CIC is a non-profit membership organisation, founded on a belief in collaborative engineering for the safe and responsible development of foundational AI technologies. A place where AI startups, enterprises large and small, public sector bodies and academia can share resources and knowledge, to codevelop and grow businesses, fast.
We support technology-focused start ups, each with unique data management challenges, and are seeking an experienced Infrastructure Engineer to help them design, deploy and maintain high-performance storage systems for their AI and data-driven workloads. The successful candidate will combine deep experience architecting and managing distributed, cloud, and tiered storage solutions with strong Linux and automation skills.
In this role you will:
- Design, implement, and maintain storage platforms that support large-scale AI and data pipelines
- Manage distributed storage systems such as Ceph, Lustre, or BeeGFS.
- Oversee tiered storage architectures, optimising data movement across high-performance, object, and archival tiers.
- Ensure data integrity, availability, and security across on-premises and cloud environments.
- Develop automation and monitoring tools using Bash, Python, or similar scripting languages.
- Manage and secure container images and related storage used for AI and ML workloads.
- Integrate storage systems with public cloud services (AWS, Azure, GCP) and hybrid environments.
- Troubleshoot complex storage and data flow issues, collaborating closely with AI platform and infrastructure teams.
- Contribute to ongoing architecture improvements, performance tuning, and capacity planning.