Senior Cloud & Reliability Leader (SRE, AI)

EY

Seattle (WA)

Hybrid

USD 144,000 - 359,000

Full time

2 days ago

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Benefits offered by this job

Medical and dental coverage

401(k) plan

Paid time off

Flexible vacation policy

Job summary

EY is seeking a senior individual contributor to lead cloud foundations and reliability across AI-enabled services within Challenger Core. You will drive architecture reviews, automation, and cost-aware implementations while guiding teams through complex decisions and serving as a hands-on technical authority.

In this role, you will craft observability, resilience, and performance patterns, mentor engineers, and publish reusable runbooks.

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.
Evidence of investigating difficult failures, supporting response, and converting lessons into durable remediation.
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 without formal delivery ownership.
Up to 50% travel and hybrid to office as needed.

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.