Senior Cloud Reliability Engineer (AI-Driven)

EY

Washington (District of Columbia)

Remote

USD 144,000 - 359,000

Full time

2 days ago

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Job summary

EY is seeking a senior infrastructure leader to drive cloud foundations, AI-assisted engineering, and reliability across complex services. This hands-on role requires deep cloud expertise, architectural judgment, and mentoring across teams within Challender Core.

The successful candidate will balance performance, cost, security, and resilience while guiding critical design decisions. The role offers hybrid collaboration across offices while allowing significant time in remote work.

Qualifications

Bachelor’s Degree in a relevant field.
9-12+ years of relevant experience.
Substantial experience designing and operating cloud, infrastructure, platform, or site reliability capabilities in production.
A record of solving complex distributed-system problems and explaining why the chosen solution will work.
Hands-on expertise with infrastructure as code, modern runtime platforms, networking, identity, and delivery automation.
Experience defining service objectives and using observability, capacity planning, and recovery testing to improve live systems.
Strong coding skills and the ability to validate AI-generated or otherwise automated engineering output independently.
Experience mentoring engineers and guiding architecture or readiness decisions.

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.