Director, Product AI Platform Architect

About the role

Applied Materials is seeking a senior leader to own the architecture and technical delivery of a unified AI platform that serves multiple business units in an air‑gapped environment. The role combines strategic design with hands‑on development across cloud, on‑premises and edge deployments.

Key responsibilities

• Define and maintain the target‑state reference architecture, including domain‑driven decomposition, API‑first and event‑driven boundaries, documented with C4 views and decision records.
• Architect a flexible deployment model that runs on‑premises, private cloud, and as a multi‑tenant public‑cloud service, using configuration‑driven hosting.
• Set Kubernetes and micro‑services standards: Helm packaging, operator patterns, GitOps reconciliation, service mesh where justified, and scaling, caching and sharding strategies.
• Design AI model serving architecture for customer hardware and cloud, covering model selection, quantization, GPU inference, continuous batching, registry and versioning.
• Own retrieval‑augmented generation design, including embeddings, vector stores, hybrid retrieval, reranking and deterministic tool‑use patterns.
• Establish AI assurance practices: golden datasets, evaluation harnesses, groundedness metrics, drift detection and prompt‑injection defenses.
• Lead CI/CD and MLOps pipelines: trunk‑based development, automated testing, container scanning, SBOM, progressive delivery, IaC, model lineage and drift monitoring.
• Translate business problems into domain models, capability roadmaps and architecture decisions, and chair architecture governance across business units.

Required profile

• 15+ years of software engineering experience, including 8+ years in architecture roles and 5+ years leading engineering organizations.
• Proven ownership of large‑scale distributed platform architecture covering multi‑tenancy, resilience, API/event contracts and deployment flexibility.
• Hands‑on experience with public‑cloud (AWS and/or Azure), Kubernetes/OpenShift, container packaging, GitOps and lifecycle management.
• Demonstrated ability to deliver platform releases on committed dates across multiple squads, managing dependencies, risks and capacity.
• Experience deploying machine‑learning or LLM‑based capabilities to production, including GPU capacity planning and model optimization.

Required skills

• Java, Spring Boot
• Python
• Node.js
• .NET
• React, Angular
• Kubernetes, Helm, OpenShift
• AWS, Azure
• GitOps, CI/CD, MLOps
• GPU inference, model quantization
• Event‑driven architecture, API‑first design

What we offer

• Salary range $218,500 – $300,500 per year
• Location in Santa Clara, CA with a supportive, learning‑focused culture
• Health, wellbeing and personal‑professional growth programs