AI Platform Architect Leader – Cloud & Observability

EY; Buffalo (NY); Hybrid; USD 144,000 - 329,000; Full time

Benefits offered by this job

Medical and dental coverage

401(k) plan

Pension

Paid time off

Flexible vacation policy

Job summary

EY is seeking a senior leader to architect and operate a scalable AI platform that spans cloud, hybrid, and edge environments. You will define the platform roadmap, set non-functional standards, and work across stakeholders to deliver durable, production-ready systems.

The role emphasizes portability of model access, resilient runtimes, and evidence-driven release patterns, with a focus on improving time-to-value for teams while maintaining strong security and governance.

Qualifications

• Bachelor's degree in a relevant field is required.
• Substantial experience designing and operating cloud, infrastructure, SRE, or platform capabilities, including production AI workloads.
• Proven track record delivering a shared platform adopted by multiple product teams with measurable improvements.

Responsibilities

• Set the platform direction: vision, reference architecture, roadmap, and non-functional standards.
• Run the platform as a product: define service tiers, prioritise the roadmap, measure adoption and value.
• Enable portable model access with gateways, routing, resilience, and transparent trade-offs.
• Architect elastic foundations for AI workloads balancing performance, resilience, and cost.
• Automate delivery with repeatable patterns, progressive release, and evidence integration.

Skills

Systems architecture

AI platform depth

Platform product mindset

Operational excellence

Trust by design

Influential leadership

Education

Bachelor's Degree in a relevant field

Job description

EY is seeking a senior leader to architect and operate a scalable AI platform that spans cloud, hybrid, and edge environments. You will define the platform roadmap, set non-functional standards, and work across stakeholders to deliver durable, production-ready systems.

The role emphasizes portability of model access, resilient runtimes, and evidence-driven release patterns, with a focus on improving time-to-value for teams while maintaining strong security and governance.