AI Native Cloud and Platform Architect - Senior Manager
$144,000 - $374,000 per year
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Posted today
SENIORITY
Manager
SALARY
$144,000 - $374,000 per year
About the role
- 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 trade-offs.
- To qualify you must have
- 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.
- Platform delivery: A record of building a shared platform adopted by multiple product teams, with measurable improvement in speed, reliability, or cost.
- Cloud architecture: Deep experience in at least one major cloud and strong judgment about portable patterns for hybrid or multi-cloud constraints.
- Hands-on engineering: Practical depth in orchestration, networking, identity, infrastructure as code, delivery automation, APIs, and production observability.
- AI operations: Experience 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: This role requires up to 50% travel and flexibility to go into the office in a hybrid capacity.
- Ideally, you'll also have
- Inference at scale: Experience operating high-volume or latency-sensitive inference and optimizing accelerator capacity across regions or environments.
- Agent platforms: Experience with agent runtimes, tool gateways, sandboxed execution, or policy-aware multi-agent environments.
- Cost and performance: Practical FinOps and performance-engineering experience for AI or other compute-intensive workloads.
- Industry contribution: Contributions to cloud-native, observability, AI platform, interoperability, open-source, standards, or research communities.
- Broader perspective: Experience translating emerging capabilities across multiple providers or technology ecosystems into stable enterprise patterns.
- We welcome exceptional candidates from product, service design, operations, transformation, policy, domain, and entrepreneurial backgrounds; no particular degree or certification is required.
- What we offer you
- At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.
- We offer a comprehensive compensation and benefits package where you'll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job is:
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