Lead Cloud Platform Engineer
- Toronto, ON, Canada
Full-time
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Job details
- Job type
- Full-time
- Apply by
- Posting language
- English
- Posted
Original job description
Description
AWS customer develops and manages several HPC clusters across AWS, CoreWeave, GCP, and other providers. Several thousand GPUs today, scaling to 10X in 2026 and beyond.
This role is Kubernetes heavy. You'll operate multi cloud platform infrastructure where misconfigurations or failed upgrades translate directly into thousands of lost GPU hours. The clusters are large enough that novel failure modes are routine.
Responsibilities
Operate Kubernetes platforms (EKS, CKS, GKE) at significant scale across providers. You're responsible for cluster lifecycle, node pool management, networking policy, and maintaining stability during rapid growth.
Provision HPC infrastructure through CI/CD system across AWS, CoreWeave, GCP, and OCI, with additional providers to be expanded in the near future.
Manage job scheduling to allocate GPU compute across training and inference workloads.
Define and maintain SLIs/SLOs. Build monitoring and alerting. Participate in severity escalation response and author post incident reviews.
Coordinate daily with Networking, Storage, Security, and AI/ML platform teams.
Requirements
4+ years in infrastructure engineering, cloud platforms, or HPC.
Kubernetes is the core requirement. You should have hands on experience operating clusters at meaningful scale: node pool sizing, scheduler debugging, CNI troubleshooting, and rolling upgrades across large fleets. Candidates whose K8s experience is limited to small or local environments are unlikely to be a fit.
Terraform proficiency. You'll write and review infrastructure as code daily.
Working knowledge of AWS (EC2, S3, EFS, FSx for Lustre).
Python for tooling and automation.
More about this job
Responsibilities
Operate and scale Kubernetes platforms and HPC infrastructure across AWS, CoreWeave, GCP, OCI, and other providers, including cluster lifecycle, node pools, networking policies, and GPU job scheduling. Define reliability objectives, build monitoring and alerting, respond to incidents, write post-incident reviews, and coordinate with networking, storage, security, and AI/ML teams.
Requirements
Requires at least four years of infrastructure engineering, cloud platform, or HPC experience, with hands-on Kubernetes operations at significant scale, including node pool sizing, scheduler debugging, CNI troubleshooting, and fleet upgrades. Candidates should be proficient with Terraform, have working knowledge of AWS services such as EC2, S3, EFS, and FSx for Lustre, and use Python for tooling and automation.
Skills
- Kubernetes
- Cloud Platform Engineering
- HPC Infrastructure
- Amazon EKS
- CoreWeave
- Google Kubernetes Engine
- Terraform
- AWS
- EC2
- S3
- EFS
- FSx for Lustre
- Python
- CI/CD
- GPU Job Scheduling
- Monitoring and Alerting
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Engineering
- Software
Keywords
- Kubernetes
- HPC
- AWS
- CoreWeave
- GCP
- OCI
- Amazon EKS
- CKS
- GKE
- Terraform
- Python
- CI/CD
- GPU
- Cluster Lifecycle
- Node Pool Management
- Kubernetes Scheduler
- CNI
- Networking Policies
- EC2
- S3
- EFS
- FSx for Lustre
- Job Scheduling
- Training Workloads
- Inference Workloads
- SLIs
- SLOs
- Monitoring
- Alerting
- Incident Response