Lead Applied AI Site Reliability Engineer II - PxE ERM

About the role

The Lead Applied AI Site Reliability Engineer II plays a pivotal 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

• Outcome-Driven Accountability: Drive reliability, performance, and cost outcomes measured in service-level objectives and error budgets, ensuring high-quality, lean operational designs that keep production safe and resilient.
• Technical Leadership and Advocacy: Serve as the technical advocate for production reliability and operability, ensuring systems are admissible, performant, safe to run, and can degrade gracefully when failure occurs. Set production standards, lead the design of observability, performance and resilience testing, and operational tooling, and own the admission of systems into production.
• Engineering Craftsmanship: Maintain accountability for the operational integrity of production and pre-production environments, and for the production standards that systems are admitted against. Own SLOs 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.
• Customer-Centric Engineering: 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.
• Advanced Technical Proficiency: 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.
• Effective Communication and Influence: Exhibit exceptional communication skills, capable of articulating complex technical concepts clearly and compellingly. Inspire and influence teammates and product teams through well-structured arguments and trade-offs supported by evidence. Create coherent narratives that align technical solutions with business objectives.

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, observability stacks, and cloud-native engineering on Azure, AWS, or GCP.
• 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 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.
• Prior 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 software engineering experience with the understanding of Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and code instrumentations, and AI-augmented spec-driven development.
• 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.

Benefits

The role offers competitive compensation, comprehensive benefits, and opportunities for professional growth and development.

Pay

$113,100 to $232,300 annually.

Schedule

Minimum of 3 days per week in the office, with occasional travel required.