AI Native Cloud and Platform Architect - Senior Manager

Job :
New York, NY, US, Salary: $172,800 - $359,000

Job Function:
Executive Management

Employer:

EY Global Services Apply by:
Company: EY

Location:

Anywhere in Country At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go.  Join EY and help to build a better working world.

The opportunity

The next generation of AI services needs a platform that can absorb rapid change without passing every new model, runtime, and control challenge to delivery teams. You will architect that foundation, bringing portable model access, dependable agent execution, automated release evidence, deep observability, and elastic infrastructure into one coherent product.

This is a high-impact role for a systems thinker who wants to stay close to code, telemetry, capacity, and users. You will shape durable patterns across cloud and hybrid environments, align senior stakeholders on practical trade-offs, and make the trusted route from an idea to a production outcome the route teams naturally choose.

About Challenger Challenger is a new EY program focused on building the firm’s next generation of services. It pursues opportunities where AI enables a fundamentally different service and way of delivering it, including rethinking existing offerings and developing new ones.

Small, dedicated teams build and test ideas in real conditions. Solutions that demonstrate clear business value and operational readiness transition to the business for adoption and scale.

Challenger Core develops and maintains the shared technology, methods, and risk and quality guardrails that support this work. Each team contributes reusable tools and practices, helping make the next service faster and less costly to build.

Your key 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.

Skills and attributes for success

Systems architecture:
Deep understanding of distributed systems and cloud-native architecture, with sound judgment about where abstraction creates value or hidden coupling.

AI platform depth:
Strong knowledge of model serving, inference gateways, AI lifecycle automation, evaluation integration, and agent runtime patterns.

Platform product mindset:
Ability to turn developer needs into intuitive self-service capabilities, durable interfaces, and an adoption-focused roadmap.

Operational excellence:
Practical command of observability, SRE, performance, capacity, resilience, recovery, and incident learning.

Trust by design:
Ability to embed security, policy, delivery assurance, and traceable evidence into platform architecture and engineering workflows.

Influential leadership:
Clear communication, pragmatic decision-making, and the ability to align executives and technical teams around complex…