Lead Applied AI Site Reliability Engineer II - PxE ERM

About the role

The Lead Applied AI Site Reliability Engineer II plays a critical role in ensuring the reliability, performance, and operational integrity of high-visibility products and platforms. This role requires a hands-on approach to managing production systems, setting production standards, and driving operational excellence.

Responsibilities

• Embrace and drive a culture of accountability for reliability, performance, and cost outcomes, measured in service-level objectives and error budgets.
• Operate the products, platforms, and environments you support to meet their Service Level Indicators (SLIs) within budget, and track incident trends and toil to prioritize work that most improves reliability.
• Set production standards, lead the design of observability, performance and resilience testing, and operational tooling, and own the admission of systems into production-gating release on error budgets and automated reliability checks.
• Maintain accountability for the operational integrity of production and pre-production environments, and for the production standards that systems are admitted against. Own Service Level Indicators (SLIs) and error budgets; build and operate production observability-codified, version-controlled dashboards and SLO-driven, actionable alerting that detects before impact, plus the feedback loop into engineering.
• Run performance, ambient-noise, and chaos testing to verify readiness; and guard environments against drift. Stay hands-on, self-driven, and continuously learn new approaches, languages, and frameworks-operating as an infrastructure-focused engineer, not a tool operator.
• Create technical specifications, runbooks, and shared playbooks; lead blameless postmortems that turn incidents into learning and systemic fixes; write high-quality, supportable automation; and review the work of other engineers, mentoring them, to ensure all reliability KPIs (availability, performance, and cost) are met or exceeded.
• Develop lean operational solutions through rapid, inexpensive experimentation to meet the reliability needs of the engineering teams and the business. Engage with those teams before, during, and after delivery, co-defining service-level objectives and operational readiness so the right safeguards are in place at the right time, without becoming a bottleneck to delivery.
• Avoid big-bang interventions, and keep operations supportable and maintainable. Foster a collaborative environment that enhances team synergy and innovation.
• Possess deep expertise in site reliability and modern production engineering-cloud platform ownership, observability, performance and capacity engineering, chaos engineering, and cloud/AI cost engineering-together with applied AI fluency to operate AI and agentic workloads reliably, including AI and Agentic SSDLC, delivering production operations with full automation from discovery to production to operations and all quality checks through the SSDLC lifecycle.

Qualifications

• A bachelor's degree in computer science, software engineering, data science, machine learning, or related discipline.
• 6+ years of software engineering and site reliability engineering experience operating large-scale, distributed, cloud-native systems in production, with experience in Python, Go, Bash, Java, C#, SQL/NoSQL, Kubernetes, Terraform, ArgoCD, CI/CD, and observability stacks.
• 3+ years of experience in site reliability or production engineering for large-scale systems-defining and owning SLIs, SLOs, and SLAs; error budgets; incident command and on-call; building and operating production observability (metrics, tracing, logging); environment integrity and drift prevention across pre-production and production; and segregation-of-duties controls (least-privilege/RBAC, deploy approvals, secrets management).
• 3+ years of experience with cloud-native engineering and cloud platform ownership on any of the cloud hyperscalers such as Azure, AWS, or GCP-including their AI/ML services and container orchestration (Kubernetes, Docker), infrastructure-as-code, networking, and multi-environment management.
• 1+ years of experience establishing reliability and operational standards-SLO discipline, runbooks, and performance and resilience budgets-including actively leading, mentoring, and guiding team members in the adoption and continuous improvement of these standards.
• Prior experience operating AI/ML and agentic workloads in production-their reliability failure modes (drift, train/serve skew, output variance), MLOps/LLMOps, and the AI control plane (model/LLM gateway, guardrails) from the operability and performance side.
• 1+ years of experience with load and performance testing under simulated production traffic (e.g., LoadRunner, k6, or JMeter), chaos engineering (e.g., Azure Chaos Studio, AWS Fault Injector), capacity planning, autoscaling, and cloud/AI cost engineering (FinOps tooling/dashboards, including GPU/inference and token cost attribution).
• Prior experience using methodologies & tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (e.g. LangFuse, LangSmith, or equivalent multi-agent orchestration tools) etc. to operate high-quality, resilient platforms and products at scale.