Principal DevOps Engineer (AI/ML Infrastructure)

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Principal DevOps Engineer (AI/ML Infrastructure) based in Canada We are looking for an experienced Principal DevOps Engineer to design, build, and manage the infrastructure powering advanced AI and machine learning capabilities within a modern SaaS platform. In this senior technical role, you will drive the development of scalable, secure, and reliable AI/ML environments, supporting the entire lifecycle from experimentation and data ingestion to model deployment and production monitoring. You will work closely with AI researchers, software engineers, and cross-functional R&D teams to bridge the gap between research and production-ready solutions. Your expertise will help optimize infrastructure performance, automate complex workflows, and accelerate AI innovation. You will also serve as a technical leader and mentor, promoting engineering best practices and driving continuous improvement. This is an opportunity to contribute to impactful scientific software solutions within an international, innovation-driven environment. Accountabilities - Architect, design, and lead the implementation of scalable, secure, resilient, and high-performance infrastructure to support demanding AI and machine learning workloads. - Build, maintain, and optimize specialized CI/CD pipelines for AI/ML applications, covering the complete lifecycle from data ingestion and model development to deployment, serving, and monitoring. - Design and manage cloud-based AI/ML infrastructure, leveraging containerization, orchestration, and platform engineering technologies to ensure scalability and operational efficiency. - Drive automation across infrastructure provisioning, model deployment, testing, and operational workflows to improve reliability, consistency, and development speed. - Collaborate closely with AI researchers, data scientists, and software engineers to streamline experimentation processes and translate machine learning models into reliable production applications.

- Ensure the security, availability, reliability, and performance of AI/ML systems through effective infrastructure management, monitoring, and continuous optimization. - Act as a technical subject matter expert, mentoring junior engineers and promoting best practices in AI/ML platform development, source control, automation, and deployment. - Identify emerging technologies, tools, and industry trends, evaluating innovative solutions that can strengthen infrastructure capabilities and accelerate AI/ML development. Requirements: - At least 5 years of professional experience in DevOps, machine learning infrastructure, or platform engineering, including several years in a senior technical or leadership capacity. - At least 5 years of advanced experience with one or more major cloud platforms, such as AWS, Microsoft Azure, or Google Cloud Platform (GCP), including their AI/ML and containerization services. - Minimum 5 years of hands-on experience with machine learning orchestration and platform technologies such as Kubeflow, MLflow, or Amazon SageMaker. - Approximately 5–8+ years of strong expertise in Docker and Kubernetes, particularly for deploying, managing, and scaling AI/ML workloads. - Approximately 5–8+ years of programming experience with languages such as Python, Go, or Bash, with a strong focus on scripting and infrastructure automation. - Experience with infrastructure-as-code and configuration management tools such as Terraform or Ansible. - Demonstrated ability to design and implement reliable automated CI/CD pipelines using tools such as GitHub Actions, GitLab CI/CD, or Jenkins. - Strong understanding of DevOps principles, cloud infrastructure, container orchestration, automation, scalability, and security best practices.