Solutions Architect (Cloud Infrastructure and MLOps)

Location: Remote from Canada. Applicants must be authorized to work in Canada and provide proof of employment eligibility.

On-target earnings: $235,000–$300,000 CAD per year.

Company

Cloud infrastructure company building a full-stack AI cloud platform for data processing, model training, and production deployment, with capabilities spanning GPU orchestration, inference optimization, compute, storage, networking, and applied AI.

What you will do

• Advise clients throughout engagements through technical consultations, proofs of concept, workshops, presentations, and training.
• Translate customer business requirements into solution architectures for cloud infrastructure and MLOps use cases.
• Design and document infrastructure-as-code solutions, technical documentation, and implementation guides.
• Optimize ML pipelines for performance, scalability, and efficient use of GPU cloud resources.
• Provide customer-scenario expertise to product, technical support, and marketing teams.
• Support hackathons, conferences, workshops, webinars, and other marketing events.

Requirements

• 5–10+ years of experience as a cloud solutions architect, systems or network engineer, developer, or in a similar cloud-focused technical role.
• Bachelor’s degree or foreign equivalent in a related field, or an equivalent combination of education and relevant experience.
• Hands-on experience with infrastructure as code and configuration management, preferably Terraform and Ansible.
• Experience with Kubernetes and Python programming.
• Understanding of GPU computing for ML training and inference, including GPU drivers, libraries, CUDA, and OpenCL.
• Strong communication skills and a customer-centric mindset.

Nice to have

• Experience with HPC or ML orchestration frameworks such as Slurm or Kubeflow.
• Experience with TensorFlow or PyTorch.
• Knowledge of cloud ML tools from NVIDIA, AWS, Azure, or Google.

Culture & Benefits

• Competitive compensation and benefits package.
• Career growth and learning opportunities.
• Flexible work with ownership and autonomy.
• Collaborative, innovative, and international environment.
• Opportunity to work on impactful AI projects.