Site Reliability Engineer - AI Agents

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Site Reliability Engineer – AI Agents based in Canada. This role sits at the intersection of platform engineering, site reliability, and applied artificial intelligence, focused on building the infrastructure that powers production-grade AI agent systems at scale. You will be responsible for ensuring that agentic workflows—both internal and customer-facing—run reliably, securely, and efficiently in production environments. The position blends deep infrastructure ownership with modern AI system challenges, requiring strong operational discipline and comfort working with rapidly evolving technologies. You will collaborate closely with AI, data engineering, and product teams to transform experimental agent capabilities into hardened, scalable systems. Beyond reliability and operations, the role emphasizes building developer platforms, APIs, and tooling that enable seamless consumption of AI infrastructure across engineering teams. This is a high-impact opportunity to shape foundational systems powering next-generation AI agent ecosystems. Accountabilities: You will be responsible for designing, operating, and scaling the infrastructure layer that powers AI agent systems in production, ensuring reliability, observability, and developer usability across the platform. Design, build, and operate scalable cloud infrastructure supporting AI agent execution, orchestration, and model serving in production Ensure reliability, performance, and observability of distributed agentic systems across internal and external products Develop platform services, APIs, SDKs, and self-service tooling to enable efficient consumption of AI infrastructure Manage compute, orchestration, and deployment infrastructure supporting AI and ML workloads at scale Build and maintain CI/CD pipelines for reliable, automated deployment of AI services and agent workflows Implement Infrastructure as Code using tools such as Terraform to provision and manage AWS environments Design and operate monitoring, logging, alerting, and incident response systems tailored to AI/ML workloads Define reliability patterns, guardrails, and failure recovery mechanisms for LLM and agent-based systems Collaborate with AI and Data Engineering teams to evolve experimental prototypes into production-grade systems Manage Kubernetes-based container orchestration environments for scalable deployment of services Implement security controls, access management, and infrastructure best practices across systems Document architecture, runbooks, and operational procedures to support platform adoption and reliability Requirements The ideal candidate is a strong SRE or platform engineer with experience in cloud-native systems, production infrastructure, and exposure to ML or AI-driven workloads. 5+ years of experience in Site Reliability Engineering, Platform Engineering, Infrastructure Engineering, or similar roles in production environments Hands-on experience supporting ML infrastructure, model serving, or MLOps pipelines in production Experience building developer platforms, internal tools, APIs, or SDKs used at scale by engineering teams Strong understanding of platform engineering principles, including self-service infrastructure and developer experience design Proficiency with Infrastructure as Code tools, particularly Terraform Strong experience with Kubernetes and containerized systems (Docker) Solid cloud infrastructure experience, preferably AWS Strong scripting and programming skills (Python preferred, plus bash/shell proficiency) Experience designing and operating observability, monitoring, and alerting systems Experience with incident response processes and on-call operational ownership Strong collaboration skills across AI, data, and engineering teams High ownership mindset with ability to operate in fast-paced, high-stakes production environments Familiarity with AI agent systems, LLM-based applications, or orchestration frameworks is a strong plus Benefits Competitive compensation package with performance-based incentives Fully remote-friendly structure with flexibility across eligible regions Comprehensive health coverage including medical, dental, and vision (where applicable) Retirement savings plans with employer contributions (where applicable) Flexible PTO policy and paid company holidays Mental health and wellness support programs Learning and development budget for continuous technical growth Opportunity to work on cutting-edge AI agent infrastructure at global scale High-ownership engineering culture with strong cross-functional collaboration Exposure to advanced platform engineering and applied AI systems. 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