Senior AI Platform Architect - Cloud & Hybrid

EY

Tampa (FL)

Hybrid

USD 144,000 - 374,000

Full time

9 days ago

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Job summary

EY is seeking a senior platform architect to lead a next-generation AI services platform in a hybrid, multi-cloud environment. You will shape durable patterns, align stakeholders on critical trade-offs, and guide teams from idea to production outcomes with a focus on reliability and cost efficiency.

The role emphasizes deep systems thinking, observability, and a balance between architectural rigor and practical delivery.

Qualifications

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 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.
Hands-on engineering: orchestration, networking, identity, infrastructure as code, delivery automation, APIs, and production observability.
AI operations: operating model-serving and AI lifecycle capabilities, including release controls, evaluation, capacity, service objectives, and incident learning.
Architecture judgment: demonstrated ability to make build-versus-buy decisions and explain the operational, commercial, and technical consequences.
Cross-functional leadership: experience leading complex programs, influencing senior stakeholders, and raising engineering capability through mentoring.
Travel and On-site requirements: 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.