Lead Applied AI Site Reliability Engineer II - PxE A&A

Experteer Overview As a Lead Applied AI SRE II, you will own reliability, performance, and cost outcomes for high-visibility products and AI-infused systems. You’ll lead with strong engineering craft across cloud platforms, observability, and AI-enabled workloads, driving scalable, resilient production. You’ll set standards, mentor teams, and advocate for production readiness and blameless learning. This role blends hands-on engineering with cross-functional leadership to deliver value through reliable, cost-aware operations. Compensation / Benefits * Own SLOs, error budgets, and incident trends to improve reliability and reduce toil. * Lead design of observability, performance and resilience testing, and operational tooling; gate production admissions using automated reliability checks. * Maintain production and pre-production environments, build SLO-driven dashboards, and drive runbooks and postmortems. * Co-create service-level objectives with engineering teams and ensure readiness for production releases. * Foster cross-functional collaboration with engineering, security, data governance, and leadership to align on standards and controls. * Advance production engineering practices including chaos testing, capacity planning, and cloud/AI cost engineering. * Mentor and review peers to ensure reliability KPIs (availability, performance, cost) are met or exceeded. * Communicate complex technical concepts clearly to stakeholders and influence decision making. Tasks * 6+ years of software and site reliability engineering experience with large-scale, cloud-native systems. * Proficiency in Python, Go, Bash, Java, C#/.NET, SQL/NoSQL; strong Kubernetes, Terraform, ArgoCD experience; CI/CD and observability stack. * 3+ years owning SLIs, SLOs, SLAs, incident command, on-call, and production observability tools (OpenTelemetry, Prometheus, Grafana, Datadog, Dynatrace, CloudWatch, Azure Monitor, Google Cloud Operations). * 3+ years cloud-native engineering on Azure/AWS/GCP including AI/ML services and container orchestration; IaC, networking, multi-environment management. * 1+ year establishing reliability standards (SLO discipline, runbooks, performance budgets) and mentoring teams. * Experience operating AI/ML workloads, including MLOps/LLMOps, AI control plane, and production reliability considerations. * Experience with load testing, chaos engineering, capacity planning, autoscaling, and cloud/AI cost attribution tooling. * Software engineering fundamentals (OOP/OOD, data structures, algorithms) and AI-augmented development; familiarity with XP/Lean/DevSecOps/SRE tooling. Key requirements * Discretionary annual incentive program

for production releases. * Foster cross-functional collaboration with engineering, security, data governance, and leadership to align on standards and controls. * Advance production engineering practices including chaos testing, capacity planning, and cloud/AI cost engineering. * Mentor and review peers to ensure reliability KPIs (availability, performance, cost) are met or exceeded. * Communicate complex technical concepts clearly to stakeholders and influence decision making. Tasks * 6+ years of software and site reliability engineering experience with large-scale, cloud-native systems. * Proficiency in Python, Go, Bash, Java, C#/.NET, SQL/NoSQL; strong Kubernetes, Terraform, ArgoCD experience; CI/CD and observability stack. * 3+ years owning SLIs, SLOs, SLAs, incident command, on-call, and production observability tools (OpenTelemetry, Prometheus, Grafana, Datadog, Dynatrace, CloudWatch, Azure Monitor, Google Cloud Operations). * 3+ years cloud-native engineering on Azure/AWS/GCP aaaaaaaw _ AI/ML services and container orchestration; IaC, networking, multi-environment management. * 1+ year establishing reliability standards (SLO discipline, runbooks, performance budgets) and mentoring teams. * Experience operating AI/ML workloads, including MLOps/LLMOps, AI control plane, and production reliability considerations. * Experience with load testing, chaos engineering, capacity planning, autoscaling, and cloud/AI cost attribution tooling. * Software engineering fundamentals (OOP/OOD, data structures, algorithms) and AI-augmented development; familiarity with XP/Lean/DevSecOps/SRE tooling. Key requirements * Discretionary annual incentive program