Senior AI Platform Architect & Cloud Lead

EY

Trenton (NJ)

Hybrid

USD 180,000 - 320,000

Full time

14 days+

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Job summary

EY is seeking an experienced Platform Architect to lead the AI platform across cloud and hybrid environments. You will own the platform vision, define architectural standards, and shape durable patterns for scalable production AI workloads.

This high-impact role balances coding, telemetry, capacity planning, and stakeholder alignment. The Challenger Core team builds shared technology and guardrails; you will mentor engineers and drive adoption of portable, secure, observable platforms that

Qualifications

Bachelor’s degree in a relevant field is required.
9-12+ years of relevant experience in cloud, infrastructure, SRE, or platform capabilities.
Substantial design and operation of cloud, infrastructure, SRE, or platform capabilities including production AI workloads.
Proven platform delivery with adoption by multiple product teams and measurable speed or reliability gains.
Deep experience in at least one major cloud and portable hybrid or multi-cloud patterns.
Hands-on engineering depth in orchestration, networking, IaC, delivery automation, APIs, and observability.
Experience operating model-serving and AI lifecycle capabilities including release controls and incident learning.
Ability to make build-versus-buy decisions and explain operational/technical trade-offs.
Cross-functional leadership with ability to influence senior stakeholders; willingness to travel up to 50% and work in hybrid capacity.

Responsibilities

Set the platform direction, AI platform vision, reference architecture, roadmap, and non-functional standards.
Run the platform as a product; define service tiers, prioritize the roadmap, and measure adoption and time to value.
Enable portable model access with gateways, stable contracts, and policy-aware routing.
Architect elastic foundations for AI workloads balancing performance, cost, and resilience.
Automate production delivery with repeatable patterns, progressive release, and evidence.
Define trusted agent runtimes with secure execution patterns and recoverability.
Engineer for reliability with service objectives, observability standards, and incident learning.
Make economics visible by linking usage, performance, and quality signals.
Guide build-versus-buy and portability decisions with cross-functional partners.
Build capability by mentoring engineers and turning emerging tech into reusable patterns.