Director, Product AI Platform Architect

Who We Are

Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology.

What We Offer

Salary:

$218,500.00 - $300,500.00

Location:

Santa Clara,CA

You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more.

At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits .

Position Summary

We are developing a single AI platform that serves multiple business units, running inside an air-gapped environment.

This role owns the architecture and the technical delivery of the platform. You will interface with senior architects and principal AIML Ops engineers, set technical direction, work across business units to turn business problems into common platform capability, and stay hands-on to lead development.

Core Responsibilities

• Platform architecture. Own the target-state reference architecture: domain-driven decomposition, bounded contexts, ports and adapters, API-first and event-driven boundaries, documented as C4 views and decision records.
• Deployment flexibility. Architect one system that runs on premises in restricted networks, in private cloud, and as a multi-tenant service in public cloud, with hosting model set by configuration rather than a fork.
• Cloud-native runtime. Set the Kubernetes and microservices standard: Helm packaging, operator patterns, GitOps reconciliation, service mesh where justified, and the scale, caching and sharding strategy for the hosted path.
• Embedded AI. Define the AI architecture for models running on customer hardware and in our cloud: open-weight model selection and quantization, GPU inference serving with continuous batching, model registry and versioning.
• Grounding and guardrails. Own the retrieval-augmented generation design covering embeddings, vector store, hybrid retrieval and reranking, with tool-use patterns that are deterministic, bounded and fully traceable.
• AI assurance. Establish evaluation and model-risk practice: golden datasets, evaluation harnesses, groundedness metrics, drift detection, prompt-injection defenses, and a clear position on build versus adapt.
• Pipeline ownership. Own CI, CD and MLOps end-to-end: trunk-based development, automated testing and quality gates, container scanning and SBOM, progressive delivery, infrastructure as code, model lineage and drift monitoring.
• Technical delivery. Hold the teams to the architecture and to the date. Own release sequencing across squads, dependency and risk management, capacity planning, and the DORA metrics that show delivery health.
• Business to architecture translation. Work with business units and customers to understand process, constraints and pain points, then convert them into domain models, capability roadmaps and architecture decisions.
• Cross-BU leadership. Serve as senior technical interface to product management, field engineering, manufacturing systems, data science, IT and InfoSec. Chair architecture governance and hold one sequenced roadmap.
• Configurability. Own the extension model: plugin and adapter interfaces, versioned APIs with backward compatibility guarantees, declarative configuration, and tenant isolation across silo, bridge and pool patterns.
• Systems integration. Architect integration to equipment and factory automation interfaces, MES, EDA and yield management systems, historians and data lakes, behind stable anti-corruption layers.
• Non-functional ownership. Own SLOs and error budgets, OpenTelemetry observability, resilience patterns such as circuit breaker and bulkhead, zero-trust segmentation, and data governance for residency, retention and egress.
• Hands-on leadership. Stay credible across the technology stack, including Java and Spring Boot, Python, Node.js, .NET, React and Angular, and lead development directly where the risk warrants it.
• Modernization. Own strangler-fig migration of legacy systems onto the common platform, a published deprecation policy, and a funded technical debt register.
• Organization. Interface with managers and principal engineers across different teams and geographical sites.

Required Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering or a related field. Master’s preferred.
• 15+ years of experience in software engineering, including 8+ years in architecture roles and 5+ years leading engineering organizations with managers reporting in.
• Demonstrated ownership of a distributed platform architecture at scale, covering domain decomposition, API and event contract management, resilience and consistency strategy, multi-tenancy and tenant isolation.
• Hands-on architecture experience across public cloud (AWS and/or Azure) and Kubernetes or OpenShift, including container packaging, orchestration, GitOps and lifecycle management.
• Experience architecting for both on-premises customer-controlled deployments and cloud-hosted delivery, including the trade-offs between them and the realities of operating under customer security governance.
• Fluency in architecture documentation and governance practice: C4 or equivalent modelling, architecture decision records, design review standards, fitness functions, NFR definition and technical debt management.
• Proven accountability for delivering a platform to committed dates across multiple squads, covering release planning, dependency and risk management, capacity planning and quality ownership. Architecture authority without delivery accountability is not sufficient for this role.
• Direct experience architecting and operating CI/CD pipelines and a production MLOps lifecycle and using engineering metrics such as DORA to diagnose and improve delivery performance.
• Deep hands-on capability across the technology stack, with the credibility to set the reference implementation, review design and code, and lead development efforts directly. This role is not purely advisory.
• Experience running a large multi-stack application portfolio to common engineering standards, and executing migration of legacy systems onto a shared platform.
• Practical experience deploying machine learning or LLM-based capability to production, including GPU capacity planning, inference serving and model optimization for constrained environments.
• Demonstrated ability to elicit and structure requirements directly from business and customer stakeholders through process mapping, current and future state analysis, use-case definition and workflow analysis, and to translate them into domain models, capability maps and architecture decisions.
• Track record of building a shared platform across business units with competing priorities, converging overlapping requirements into common capability and sustaining that alignment when local incentives pull against it.
• Ability to separate genuine platform capability from customer or BU-specific customization, and to design the configuration and extension model that absorbs the difference without forking the codebase.

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