Senior AI Platform Architect and Cloud Strategy Lead

EY

Seattle (WA)

Hybrid

USD 144,000 - 374,000

Full time

14 days+

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Benefits offered by this job

Medical and dental coverage

Pension and 401(k) plans

Paid time off

Flexible vacation policy

EY Paid Holidays

Job summary

EY is seeking a senior platform architect to lead the AI platform strategy across cloud, hybrid, and edge environments. You will shape durable patterns, govern non-functional requirements, and drive adoption while balancing cost, performance, and security.

You’ll guide multi-disciplinary teams, mentor engineers, and partner with product, security, risk, and finance to deliver trusted, portable AI capabilities at scale. A hybrid office presence is expected.

Qualifications

Bachelor’s degree in a relevant field.
Substantial experience designing and operating cloud, infrastructure, SRE, or platform capabilities, including responsibility for production AI workloads.
A record of building a shared platform adopted by multiple product teams, with measurable improvement in speed, reliability, or cost.
Deep experience in at least one major cloud and strong judgment about portable patterns for hybrid or multi-cloud constraints.
Practical depth in orchestration, networking, identity, infrastructure as code, delivery automation, APIs, and production observability.
Experience operating model-serving and AI lifecycle capabilities, including release controls, evaluation, capacity, service objectives, and incident learning.
Demonstrated ability to make build-versus-buy decisions and explain the operational, commercial, and technical consequences.
Experience leading complex programs, influencing senior stakeholders, and raising engineering capability through mentoring.
This role requires up to 50% travel and flexibility to go into the office in a hybrid capacity.

Responsibilities

Set the platform direction. Own the AI platform vision, reference architecture, roadmap, and non-functional standards across cloud, hybrid, and approved edge environments.
Run the platform as a product. Understand developer needs, define clear service tiers and golden paths, prioritize the roadmap, and measure adoption and time to value.
Enable portable model access. Design gateways and stable contracts that support model choice, policy-aware routing, resilience, and controlled experimentation without hiding important trade-offs.
Architect elastic foundations. Shape the compute, storage, and network capabilities needed for AI workloads, balancing performance, regional constraints, resilience, and cost.
Automate the path to production. Create repeatable delivery patterns that promote code and AI assets together, with progressive release, rollback, and evidence built into the process.
Define trusted agent runtimes. Establish secure execution patterns for tools, identities, approvals, isolation, recovery, and replay so agent behavior remains controlled and understandable.
Engineer for reliability. Set service objectives, observability standards, recovery patterns, and resilience exercises that make platform health measurable and operationally useful.
Make economics visible. Connect usage, performance, and quality signals so teams can improve unit economics without weakening reliability or user outcomes.
Guide strategic choices. Lead build-versus-buy and portability decisions with engineering, product, security, risk, procurement, and finance partners.
Build capability. Mentor platform engineers and SREs, strengthen architecture practices, and help teams turn emerging technology into reusable production patterns.
Additional line: This is a challenging senior role requiring broad cross-functional collaboration and a strong focus on reliability.