AI/ML Platform Engineer – Containers & GPU Infrastructure - Canada
- Toronto, ON, Canada
Full-time
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Job details
- Job type
- Full-time
- Apply by
- Posting language
- English
- Posted
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
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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
Categories
- Technology
- Software
- Engineering
- Data & Analytics
- Security & Safety
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