Senior Cloud & SRE Leader - AI-Driven, Remote-Ready

EY

Fort Worth (TX)

Hybrid

USD 144,000 - 374,000

Full time

47 hours ago

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Benefits offered by this job

Medical coverage

Dental coverage

401(k) plan

Paid time off

Flexible vacation

Job summary

EY is seeking a senior cloud engineering professional for Challenger, EY's program focused on AI-enabled services. You will shape secure, observable, and cost-aware cloud foundations, and guide architecture, performance, resilience, and cost across multiple use cases using AI-assisted tooling.

This hands-on role requires deep cloud expertise, production reliability, and the ability to lead difficult design decisions.

Qualifications

Bachelor’s Degree in a relevant field of study.
9-12+ years of 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.

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.