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Added 14 mins ago
Sr. Data Engineer, WW Pricing Data and Insights
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As a Senior Data Engineer, you will be a technical lead on the team, leading designs and mentoring engineerings on the team. you will create data models and ML data infrastructure that…
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- Contract
Professional certification as a Professional Engineer (P.Eng.), Information Systems Professional (ISP) or equivalent designation is desirable.
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- $65 to $75 CAD / hour
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IT Accel, Inc.

AI/ML Platform Engineer – Containers & GPU Infrastructure - Canada

Toronto, ON, Canada

Full-time

Apply About employer

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Job details

Job type: Full-time
Apply by: Dec 4, 2026
Posting language: English
Posted: Nov 4, 2026

Original job description

Hybrid: 4 days onsite, 1 remote.

Type: Contract-to-Hire

About The Role

We're hiring a hands-on AI/ML Platform Engineer to build and run the infrastructure behind enterprise AI/ML services at a large financial services organization. The core of the job is building containers: writing, hardening and shipping container images and GPU-enabled workloads, then running them on Kubernetes, NVIDIA GPU infrastructure and Microsoft Azure.

This is not a DevOps-only role, and it is not model development. You'll build, operate, troubleshoot and improve the platform that data science and application teams depend on, inside a secure, regulated environment.

What You'll Do

Container Engineering & Kubernetes

Build, harden and maintain container images for AI/ML workloads, including GPU-enabled images with the correct NVIDIA driver and CUDA dependencies.
Manage images in registries with versioning, vulnerability scanning and lifecycle controls.
Deploy, scale and support containerized AI/ML services on enterprise Kubernetes clusters.
Allocate GPU resources across containers and clusters and keep them well used.
Troubleshoot container runtime, networking, storage, performance and orchestration issues.
Automate container provisioning, configuration and lifecycle management.
Partner with application and platform teams to deploy containerized AI/ML services.

GPU Infrastructure

Support bare-metal GPU servers used for training, inference and production workloads.
Install, configure and troubleshoot NVIDIA GPU drivers, CUDA and NVIDIA AI Enterprise software.
Monitor GPU utilization, availability, performance and allocation.
Resolve driver, container-compatibility and workload-performance issues.
Integrate GPU infrastructure with enterprise AI/ML platforms.

Azure AI/ML Platform

Design, build and maintain AI/ML platform services on Azure, including Azure AI services.
Build MLOps/AIOps pipelines for training and production models.
Support provisioning, administration, interoperability, disaster recovery and business continuity.
Troubleshoot Azure infrastructure, DNS, connectivity, NSGs and routing.
Help application teams use AI services to meet business goals.
Keep platform services within enterprise availability and performance targets.

Automation, IaC & CI/CD

Build platform automation with Python, Terraform, GitHub Actions, Azure CLI and the Azure Python SDK.
Maintain Infrastructure as Code using Terraform and ARM templates.
Build and maintain CI/CD pipelines for infrastructure, container images and AI/ML services.
Develop and consume APIs, SDKs and webhooks to connect orchestration platforms with enterprise systems.
Keep improving platform tooling and engineering practices.

Security & Compliance

Work with cloud, network, cybersecurity and risk teams to deliver secure AI/ML solutions.
Keep cloud and container infrastructure compliant with internal policy and regulatory requirements.
Apply Azure security controls: authentication, RBAC and data protection.
Support PKI and certificate management for cloud services and applications.
Identify and reduce risks across cloud infrastructure, containers and AI/ML workloads, including the secure deployment of models and data.
Take part in monitoring, incident resolution and operational support.

Required

Hands-on experience in cloud platform, infrastructure or AI/ML platform engineering.
Proven experience building container images (Docker or equivalent) and deploying containerized workloads.
Strong Kubernetes and container orchestration experience.
Experience supporting GPU infrastructure and GPU-enabled containers, including bare-metal servers, NVIDIA drivers and CUDA.
Familiarity with NVIDIA AI Enterprise.
Strong Microsoft Azure experience.
Proficiency in Python for scripting and automation.
Hands-on Terraform, IaC and CI/CD experience, including GitHub Actions and GitHub Runners.
Solid understanding of networking, DNS, routing, cloud connectivity and infrastructure security.
Working knowledge of model deployment and MLOps concepts.
Strong troubleshooting and problem-solving skills.
Able to work independently and with cross-functional teams.

Nice to Have

AI/ML infrastructure experience in a large enterprise or financial services organization.
Production experience running NVIDIA GPU infrastructure and GPU-enabled Kubernetes clusters.
Experience with GPU scheduling, utilization monitoring and performance tuning.
Hands-on NVIDIA AI Enterprise experience.
Experience with Azure AI services and enterprise AI/ML platforms.
Background in enterprise security, governance and regulatory compliance.
Experience in Agile/Scrum and DevOps environments.

Apply About employer

More about this job

Responsibilities

Build, secure, and operate containerized AI/ML services on Kubernetes, Azure, and NVIDIA GPU infrastructure, including GPU-enabled images and workloads. Automate platform provisioning and delivery, support MLOps pipelines, troubleshoot infrastructure, and maintain security, compliance, availability, and performance.

Requirements

Requires hands-on experience with container image engineering, Kubernetes, GPU infrastructure and NVIDIA drivers/CUDA, Microsoft Azure, Python, Terraform, and CI/CD. Candidates should also understand networking and infrastructure security, have working knowledge of model deployment and MLOps, and be able to troubleshoot independently and collaborate across teams.

Skills

Container Engineering
Kubernetes
GPU Infrastructure
NVIDIA Drivers And CUDA
Microsoft Azure
Python
Terraform
Infrastructure As Code
CI/CD
GitHub Actions
MLOps
Networking And DNS
Cloud Security
Container Vulnerability Scanning
GPU Scheduling And Performance Tuning
Incident Troubleshooting

Visa sponsorship

Not detected in the job text

Keywords

AI/ML Platform Engineering
Docker
Kubernetes
NVIDIA GPU Infrastructure
NVIDIA Drivers
CUDA
NVIDIA AI Enterprise
Microsoft Azure
Azure AI Services
Python
Terraform
ARM Templates
Infrastructure As Code
CI/CD
GitHub Actions
GitHub Runners
Azure CLI
Azure Python SDK
MLOps
AIOps
Container Registries
Vulnerability Scanning
GPU Scheduling
GPU Utilization Monitoring
DNS
Network Security Groups
Routing
RBAC
PKI
Disaster Recovery

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