> Markdown version of [/jobs/ext/3898932-cloud-engineer-and-site-reliability-sme-senior-manager](https://www.wearedevelopers.com/jobs/ext/3898932-cloud-engineer-and-site-reliability-sme-senior-manager). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Cloud Engineer and Site Reliability (SME) - Senior Manager - **Company:** Ernst & Young LLP - **Location:** San Mateo, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $172,800.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Cloud Computing, Cloud Engineering, Software Debugging, Distributed Systems, Open Source Technology, Reliability Engineering, Software Tools, Service Design, Deployment Automation, Software Coding - **Published:** October 23, 2026 - **Apply:** https://dejobs.org/x/x/DD1C2A4EEFBF468C990EA4D496EFB946/job/ ## About the Role * Deep cloud expertise: Strong knowledge of cloud infrastructure and distributed systems, with the ability to reason across system layers. * Hands-on engineering: Ability to write, review, debug, and test infrastructure code, deployment automation, and production services. * Diagnostic judgment: Skill in using telemetry and controlled experiments to distinguish symptoms from causes and verify that a fix works. * Reliability leadership: Practical command of service objectives, capacity, resilience, recovery, incident learning, and operational readiness. * AI workload awareness: Understanding of how model services, retrieval, and agent workflows introduce additional dependencies, failure modes, and costs. * Technical influence: The credibility to explain difficult trade-offs, set standards through evidence, and help teams adopt better engineering practices. To qualify you must have * Bachelor's Degree in a relevant field of study. * 9-12+ years of relevant experience. * Relevant experience: Substantial experience designing and operating cloud, infrastructure, platform, or site reliability capabilities in production. * Technical depth: A record of solving complex distributed-system problems and explaining why the chosen solution will work. * Cloud engineering: Hands-on expertise with infrastructure as code, modern runtime platforms, networking, identity, and delivery automation. * Production reliability: Experience defining service objectives and using observability, capacity planning, and recovery testing to improve live systems. * Incident leadership: Evidence of investigating difficult failures, supporting response, and converting lessons into durable remediation. * Automation judgment: Strong coding skills and the ability to validate AI-generated or otherwise automated engineering output independently. * Cross-team influence: Experience mentoring engineers and guiding architecture or readiness decisions without relying on formal delivery ownership. * Travel and On-site requirements: This role requires up to 50% travel and flexibility to go into the office in a hybrid capacity. Ideally, you'll also have * AI production systems: Experience operating model-serving, retrieval, or agent workloads with demanding reliability and performance needs. * Hybrid environments: Experience designing portable patterns across cloud, hybrid, or multi-region environments. * Performance and economics: Experience connecting capacity, latency, throughput, and cost to service design and scaling decisions. * High-assurance delivery: Experience in regulated or confidential environments with formal security, resilience, or evidence requirements. * Community contribution: Practical contributions to cloud-native, infrastructure, SRE, observability, automation, or open-source communities. We welcome exceptional candidates from product, service design, operations, transformation, policy, domain, and entrepreneurial backgrounds; no particular degree or certification is required. ## Description AI-enabled services need cloud foundations that remain secure, observable, recoverable, and cost-aware as workloads change. You will serve as the hands-on technical authority for the engineering practices that move these services from experimentation into dependable production use. This is a senior individual contributor role for someone who enjoys solving the problems other teams cannot. You will use AI-assisted engineering tools to accelerate infrastructure and automation work, apply your expertise to verify the results, and guide difficult decisions about architecture, performance, resilience, recovery, and cost across multiple use cases. About Challenger Challenger is a new EY program focused on building the firm's next generation of services. It pursues opportunities where AI enables a fundamentally different service and way of delivering it, including rethinking existing offerings and developing new ones. Small, dedicated teams build and test ideas in real conditions. Solutions that demonstrate clear business value and operational readiness transition to the business for adoption and scale. Challenger Core develops and maintains the shared technology, methods, and risk and quality guardrails that support this work. Each team contributes reusable tools and practices, helping make the next service faster and less costly to build. Your key responsibilities * Build shared cloud foundations. Create reusable infrastructure, environment patterns, and deployment automation that help teams start quickly and operate consistently. * Use AI-assisted engineering responsibly. Apply AI tools to accelerate development and investigation while independently reviewing, debugging, testing, and validating the resulting code. * Guide complex designs. Review architectures, migrations, and releases where trade-offs affect reliability, security, performance, cost, or multiple use cases. * Strengthen production readiness. Define the evidence required before launch and identify what must change when a service is not ready to operate reliably. * Set reliability practices. Establish service objectives, observability standards, actionable alerts, and operational reviews that connect system behavior to user outcomes. * Make cost and capacity visible. Test workload assumptions and use performance, consumption, and demand evidence to guide scaling and architecture decisions. * Resolve difficult failures. Lead deep investigation across distributed services and integrations, verify root causes against system evidence, and test durable fixes. * Prove resilience. Design performance tests, failure simulations, recovery exercises, and rollback paths that show how services behave under stress. * Turn lessons into reusable capabilities. Publish tested reference implementations, guidance, and runbooks that state both the intended use and important limitations. * Raise engineering capability. Coach engineers, challenge assumptions constructively, and transition proven capabilities to ongoing owners with clear operating responsibilities.