Site Reliability Engineer - AI Agents

You will be responsible for designing, operating, and scaling resilient infrastructure systems that support AI agent workloads in production, ensuring reliability, scalability, and developer usability across the platform.

• Design, build, and operate cloud-native infrastructure supporting AI agent execution, orchestration, and model serving at scale
• Ensure reliability, observability, and performance of distributed agentic systems across internal and external-facing products
• Develop platform services, APIs, SDKs, and self-service tooling to enable teams to efficiently consume AI infrastructure capabilities
• Manage and optimize compute, orchestration, and serving layers for AI and ML workloads in production environments
• Build and maintain CI/CD pipelines to enable safe, fast, and reliable deployment of AI services and agent workflows
• Implement Infrastructure as Code using tools such as Terraform to provision and manage AWS-based infrastructure
• Design monitoring, alerting, and observability systems tailored to AI/ML and agent-based workloads
• Define and enforce reliability patterns, guardrails, and failure recovery mechanisms for LLM and agentic systems
• Collaborate with AI, Data Engineering, and Product teams to transform experimental prototypes into production-ready systems
• Manage Kubernetes-based container orchestration environments, ensuring scalable and efficient workload deployment
• Implement security best practices and access controls across infrastructure and platform services
• Document system architecture, operational procedures, and runbooks to support team knowledge sharing and reliability

Requirements

The ideal candidate is a strong platform-minded engineer with deep SRE experience, a solid understanding of cloud-native systems, and exposure to AI/ML infrastructure or agent-based systems.

• 5+ years of experience in Site Reliability Engineering, Platform Engineering, Infrastructure Engineering, or similar production-focused roles
• Hands-on experience supporting ML systems, model serving infrastructure, or MLOps pipelines in production environments
• Strong experience building developer platforms, internal tools, APIs, or SDKs used by engineering teams at scale
• Deep understanding of platform engineering principles, including self-service infrastructure and developer experience design
• Strong proficiency with Infrastructure as Code tools, particularly Terraform
• Advanced experience with Kubernetes and containerized environments (Docker)
• Solid cloud infrastructure experience, preferably within AWS environments
• Strong programming and scripting skills (Python preferred, plus bash/shell proficiency)
• Experience designing and operating observability, logging, monitoring, and alerting systems
• Proven experience with incident response, on-call rotations, and production reliability ownership
• Strong cross-functional collaboration skills across AI, data, and engineering teams
• High ownership mindset with the ability to operate in fast-moving, high-stakes production environments
• Familiarity with AI/agent systems, orchestration frameworks, or LLM-based applications is a strong plus

Benefits

• Competitive compensation package with performance-based incentives
• Remote-first working model across multiple eligible countries
• Comprehensive medical, dental, and vision insurance coverage (where applicable)
• Retirement savings plans with employer contribution options
• Flexible PTO policy and company holidays
• Mental health support and wellness programs
• Learning and development budget for technical and professional growth
• Opportunities to work on cutting-edge AI agent infrastructure at global scale
• Inclusive, distributed engineering culture with strong emphasis on ownership and impact
• Regular opportunities to collaborate with high-performing AI and platform engineering teams.