Principal DevOps Engineer (AI/ML Infrastructure)

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. Excellent communication and collaboration skills, with the ability to work effectively alongside AI researchers, software developers, infrastructure specialists, and business stakeholders. Strong technical problem-solving skills, initiative, and the ability to drive complex infrastructure projects independently. A valid LinkedIn profile and relevant professional work links must be included with your application. Must be based in Canada and available to work Eastern Standard Time (EST) business hours. Benefits: Competitive annual base salary: CAD $130,000–$165,000, depending on experience, qualifications, work location, and internal compensation considerations. Fully remote opportunity: Work remotely from anywhere within Canada. Full-time position: Long-term opportunity in a newly created role. Technical leadership: Take ownership of strategic AI/ML infrastructure initiatives and influence platform engineering practices. Professional development: Work with modern cloud technologies, machine learning platforms, container orchestration, and advanced automation tools. Collaborative environment: Partner with experienced AI researchers, software engineers, and R&D professionals. Innovation and impact: Contribute to the development of AI-powered scientific software solutions supporting research, discovery, and innovation. Additional employee benefits may be available depending on the position and employment package offered. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1