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AI Infrastructure Engineer

  • Canada

Full-time

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Job details

Job type
Full-time
Experience
5+ years of experience in AI infrastructure or systems engineering.
Working hours
40 hours per week
Posting language
English
Posted

Original job description

Employment type: Full-time position, 40 hours per week, with direct employment by the client.

Location: Remote-first role for candidates based in Canada.

Travel requirement: Candidates must be available to visit the client’s office in Montreal approximately two to three times per quarter.

Our company description

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About the client

An AI infrastructure company specializing in inference optimization for large-scale models.

The platform enhances token throughput and reduces latency by optimizing hardware utilization and decoding processes within a customer's cloud environment.

The technology focuses on improving the efficiency of model serving without requiring changes to existing infrastructure or code, ensuring data privacy while reducing operational costs for organizations deploying open-source models.

About the Role

As a founding Senior AI Infrastructure Engineer, you will report to the Chief Technology Officer to design and operate large-scale infrastructure for AI workloads on public cloud and on-premises environments. You will be an individual contributor with significant influence, combining software engineering with deep systems expertise to build secure and reliable platforms. Your work will focus on enabling efficient model serving at scale, ensuring the infrastructure can support a massive number of concurrent users while maintaining high service quality and performance.

What You'll Do

  • Design and manage multi-cloud and on-premises infrastructure for internal and external workloads.
  • Orchestrate large-scale deployments using infrastructure-as-code and container orchestration to support high user concurrency.
  • Benchmark AI inference workloads to identify performance bottlenecks and optimize hardware utilization.
  • Develop and maintain comprehensive monitoring systems to ensure high service quality and reliability.
  • Collaborate with customers to deploy and integrate optimization solutions within their existing infrastructure.
  • Build and maintain an internal laboratory for infrastructure testing and experimentation.

What You Bring

  • 5+ years of experience in AI infrastructure or systems engineering.
  • Proficiency in Python and experience building and operating scalable APIs.
  • Extensive experience with Kubernetes, Helm, Ansible, and Terraform.
  • Proven track record of shipping large language model (LLM) serving systems in production environments.
  • Experience with distributed computing frameworks such as Ray, RayClusters, or KubeRay.
  • Demonstrated experience with agentic workflows.
  • Bachelor’s degree in Computer Science or a related technical field.

Compensation & Benefits

  • Founding-engineer equity and direct ownership.
  • Remote-friendly work environment. Visit the office 2-3x a quarter in Montreal.
  • Access to extensive compute resources and specialized hardware for experimentation.
More about this job

Responsibilities

Design and operate multi-cloud and on-premises infrastructure for AI workloads, including large-scale deployments, monitoring, and inference benchmarking. Collaborate with customers to integrate optimization solutions and maintain an internal laboratory for infrastructure testing and experimentation.

Requirements

Candidates need at least five years of AI infrastructure or systems engineering experience, strong Python skills, and extensive experience with Kubernetes, Helm, Ansible, and Terraform. They must have production experience shipping LLM serving systems, experience with distributed computing frameworks and agentic workflows, and a bachelor's degree in Computer Science or a related technical field.

Skills

  • AI Infrastructure
  • Systems Engineering
  • Python
  • Scalable APIs
  • Kubernetes
  • Helm
  • Ansible
  • Terraform
  • LLM Serving
  • Distributed Computing
  • Ray
  • RayClusters
  • KubeRay
  • Agentic Workflows
  • Infrastructure as Code
  • Performance Optimization

Benefits

  • Founding-Engineer Equity
  • Access to Compute Resources and Specialized Hardware

Visa sponsorship

Not detected in the job text

Categories

  • Technology
  • Software
  • Engineering

Keywords

  • AI Infrastructure
  • Inference Optimization
  • Large Language Models
  • LLM Serving
  • Python
  • Scalable APIs
  • Kubernetes
  • Helm
  • Ansible
  • Terraform
  • Infrastructure as Code
  • Container Orchestration
  • Multi-Cloud
  • On-Premises Infrastructure
  • Distributed Computing
  • Ray
  • RayClusters
  • KubeRay
  • Agentic Workflows
  • Model Serving
  • Hardware Utilization
  • Performance Benchmarking
  • Monitoring
  • Cloud Environments
  • Data Privacy
  • Computer Science

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