Posted 7 days ago
Sr Kubernetes & AI Platform Engineer - Canada
@ IT Accel, Inc.
Toronto, Ontario
SIGNALS
Requirements summary:
- The ideal candidate will have deep Kubernetes expertise, hands-on NVIDIA GPU experience, and a strong background in cloud automation, AI infrastructure, security, and production operations.
- Required Qualifications
- 7+ years of experience in Cloud Engineering, Platform Engineering, DevOps, SRE, or Infrastructure Engineering.
- Strong hands-on experience designing and managing production Kubernetes environments using AKS, GKE, or GDC.
- Practical experience with NVIDIA GPUs in Kubernetes, including GPU Operator, drivers, device plugins, DCGM, MIG, and GPU scheduling.
- Strong knowledge of Kubernetes architecture, networking, storage, security, observability, and autoscaling.
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-11-03
- First seen by our source
- 2026-11-10
- Source record updated
- 2026-11-10
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
IT Accel, Inc.
| Field | Value |
|---|---|
| Open jobs on Jobsy | 3 |
Job description
Industry: Banking & Financial Services / Technology
Hybrid: 4 days onsite, 1 remote.
Type: Contract-to-Hire
Position Overview
Our client, a leading financial services organization, is seeking an experienced Senior Kubernetes & AI Platform Engineer to design, implement, and manage secure, scalable GPU-enabled Kubernetes platforms across Azure Kubernetes Service (AKS), Google Kubernetes Engine (GKE), and Google Distributed Cloud (GDC).
The ideal candidate will have deep Kubernetes expertise, hands-on NVIDIA GPU experience, and a strong background in cloud automation, AI infrastructure, security, and production operations.
Key Responsibilities
A hands-on Kubernetes and AI infrastructure engineer with proven experience deploying and managing GPU-enabled cloud platforms. The successful candidate will combine strong technical architecture skills with automation, security, performance optimization, and production support expertise.
Hybrid: 4 days onsite, 1 remote.
Type: Contract-to-Hire
Position Overview
Our client, a leading financial services organization, is seeking an experienced Senior Kubernetes & AI Platform Engineer to design, implement, and manage secure, scalable GPU-enabled Kubernetes platforms across Azure Kubernetes Service (AKS), Google Kubernetes Engine (GKE), and Google Distributed Cloud (GDC).
The ideal candidate will have deep Kubernetes expertise, hands-on NVIDIA GPU experience, and a strong background in cloud automation, AI infrastructure, security, and production operations.
Key Responsibilities
- Design, deploy, and maintain production-ready GPU-enabled Kubernetes environments across AKS, GKE, and GDC.
- Manage NVIDIA GPU Operator, drivers, device plugins, GPU scheduling, and lifecycle management.
- Implement GPU monitoring, telemetry, and alerting using NVIDIA DCGM Exporter and enterprise observability tools.
- Configure GPU sharing and resource allocation using MIG, time-slicing, and Dynamic Resource Allocation (DRA).
- Automate infrastructure provisioning, deployments, and scaling using Terraform, Helm, GitHub Actions, and scripting.
- Optimize GPU utilization, cluster autoscaling, workload performance, and infrastructure costs.
- Implement Kubernetes security controls, RBAC, policy-as-code, and compliance standards, including CIS, STIG, and Wiz.
- Troubleshoot Kubernetes, GPU, networking, storage, and container-related issues.
- Support AI/ML workloads, including model training, inference, and distributed computing.
- Develop technical documentation, operational runbooks, and disaster recovery procedures.
- Collaborate with cloud, security, infrastructure, and AI/ML engineering teams.
- 7+ years of experience in Cloud Engineering, Platform Engineering, DevOps, SRE, or Infrastructure Engineering.
- Strong hands-on experience designing and managing production Kubernetes environments using AKS, GKE, or GDC.
- Practical experience with NVIDIA GPUs in Kubernetes, including GPU Operator, drivers, device plugins, DCGM, MIG, and GPU scheduling.
- Strong knowledge of Kubernetes architecture, networking, storage, security, observability, and autoscaling.
- Experience with Terraform, Helm, GitHub Actions, and Python, Go, or Bash scripting.
- Experience implementing cloud security, Kubernetes RBAC, admission policies, and compliance controls.
- Familiarity with Prometheus, centralized logging, monitoring, and incident response.
- Strong Linux, containerization, networking, and infrastructure troubleshooting skills.
- Excellent communication and cross-functional collaboration skills.
- Experience supporting AI/ML infrastructure, MLOps, model training, and inference workloads.
- Familiarity with NVIDIA H100/H200 GPUs, GPU performance optimization, and distributed AI computing.
- Experience with KServe, NVIDIA NIM, Triton Inference Server, Ray, Kubeflow, or MLflow.
- Knowledge of Azure Machine Learning and Google Vertex AI.
- Experience with Wiz, cloud security, and regulated financial services environments.
- Certifications such as CKA, CKS, CKAD, Azure, Google Cloud, or NVIDIA.
A hands-on Kubernetes and AI infrastructure engineer with proven experience deploying and managing GPU-enabled cloud platforms. The successful candidate will combine strong technical architecture skills with automation, security, performance optimization, and production support expertise.
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