AI Native Cloud and Platform Architect - Senior Manager

EY•Hartford, DC

3d•$144,000 - $374,000•Hybrid

About The Position

EY is seeking a senior architect to shape the platform foundation for its next generation of AI services through the Challenger program. The role will bring portable model access, dependable agent execution, automated release evidence, observability, and elastic infrastructure together as a coherent product across cloud, hybrid, and approved edge environments. The architect will stay close to engineering, operations, telemetry, capacity, and user needs while establishing patterns that help teams move ideas into production reliably. Challenger develops new services and ways of delivering them where AI enables a fundamentally different approach. Small teams test ideas in real conditions, and solutions demonstrating business value and operational readiness transition to the business for adoption and scale. Challenger Core maintains shared technology, methods, and risk and quality guardrails. The role owns platform direction and product evolution, including architecture, roadmap, service tiers, developer paths, model access, infrastructure, delivery automation, agent runtimes, reliability, and platform economics. It also guides strategic technology decisions and builds engineering capability through mentoring and reusable production patterns. The position requires up to 50% travel and flexibility to work in the office in a hybrid capacity.

Requirements

Bachelor’s degree in a relevant field of study.
9–12+ years of relevant experience.
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 improvements in speed, reliability, or cost.
Deep experience in at least one major cloud and sound judgment about portable patterns for hybrid or multi-cloud constraints.
Practical engineering 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 their operational, commercial, and technical consequences.
Experience leading complex programs, influencing senior stakeholders, and raising engineering capability through mentoring.
Ability to communicate clearly, make pragmatic decisions, and align executives and technical teams around complex trade-offs.
Requires up to 50% travel and flexibility to go into the office in a hybrid capacity.

Nice To Haves

Experience operating high-volume or latency-sensitive inference and optimizing accelerator capacity across regions or environments.
Experience with agent runtimes, tool gateways, sandboxed execution, or policy-aware multi-agent environments.
Practical FinOps and performance-engineering experience for AI or other compute-intensive workloads.
Contributions to cloud-native, observability, AI platform, interoperability, open-source, standards, or research communities.
Experience translating emerging capabilities across multiple providers or technology ecosystems into stable enterprise patterns.

Responsibilities

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 by understanding developer needs, defining service tiers and golden paths, prioritizing the roadmap, and measuring adoption and time to value.
Design gateways and stable contracts for portable model access, model choice, policy-aware routing, resilience, and controlled experimentation.
Shape compute, storage, and network capabilities for AI workloads while balancing performance, regional constraints, resilience, and cost.
Create repeatable delivery patterns that promote code and AI assets together, with progressive release, rollback, and evidence built into the process.
Establish secure agent execution patterns for tools, identities, approvals, isolation, recovery, and replay.
Set service objectives, observability standards, recovery patterns, and resilience exercises to make platform health measurable and useful in operations.
Connect usage, performance, and quality signals to help teams improve unit economics while maintaining reliability and user outcomes.
Lead build-versus-buy and portability decisions with engineering, product, security, risk, procurement, and finance partners.
Mentor platform engineers and SREs, strengthen architecture practices, and help teams develop reusable production patterns.
Travel up to 50% and work in the office in a hybrid capacity as required.