MLOps Engineer — GPU Platform & Kubernetes Ops

Ericsson; Linköpings kommun; On-site; SEK 800,000 - 1,100,000; Full time

Job summary

Ericsson seeks an MLOps Engineer to support AI infrastructure on the Sferical.ai platform. You will bridge AI teams and infrastructure, ensuring 216+ GPUs are used efficiently, securely and reliably.

You’ll troubleshoot complex systems and enable smooth AI workload execution in a governed enterprise environment. You will operate GPU orchestration, troubleshoot Kubernetes, manage data movement (S3/NFS), monitor health and contribute to capacity planning.

Qualifications

• Experience with Kubernetes-based workload orchestration and RBAC.
• Hands-on experience with GPU scheduling platforms and AI workloads.
• Strong data pipeline and storage handling capabilities (S3/NFS).
• Knowledge of enterprise security, access controls and compliance workflows.

Responsibilities

• Operate and monitor GPU workload orchestration using Run:ai, managing projects, queues, resource allocation and job submissions
• Troubleshoot Kubernetes clusters, including pod scheduling, storage connectivity, deployments and runtime issues
• Support data movement across storage systems (S3, NFS) and manage access controls through enterprise identity systems
• Monitor infrastructure health, track GPU utilization and produce capacity reports for planning and governance
• Coordinate with users, internal teams and external support vendors to resolve incidents and process change requests
• Prepare infrastructure for future scaling, including Kubernetes upgrades, inference enablement and security integrations
• Create operational guides, dashboards and documentation to help teams navigate the platform

Skills

Kubernetes

GPU scheduling

Distributed training

Data pipelines

Security & compliance

Troubleshooting infra

RBAC

Tools

Run:ai

S3

NFS

VAST Data

NVIDIA platforms

Slurm

Job description

Ericsson seeks an MLOps Engineer to support AI infrastructure on the Sferical.ai platform. You will bridge AI teams and infrastructure, ensuring 216+ GPUs are used efficiently, securely and reliably.

You’ll troubleshoot complex systems and enable smooth AI workload execution in a governed enterprise environment. You will operate GPU orchestration, troubleshoot Kubernetes, manage data movement (S3/NFS), monitor health and contribute to capacity planning.