Director, Product AI Platform Architect - Santa Clara, CA, United States

Originally posted 13 October 2026 by the employer.

This role will lead the architecture and technical delivery of a single AI platform running inside an air-gapped environment.

About the role

This role owns the architecture and technical delivery of a single AI platform that serves multiple business units, running inside an air-gapped environment.

What you'll do

• Own the target-state reference architecture, including domain-driven decomposition, bounded contexts, ports and adapters, API-first and event-driven boundaries, documented as C4 views and decision records.
• Architect a system that runs on premises in restricted networks, in private cloud, and as a multi-tenant service in public cloud, with configuration-based hosting models.
• Set the Kubernetes and microservices standard, including Helm packaging, operator patterns, GitOps reconciliation, and scaling strategies.
• Define the AI architecture for models running on customer hardware and in the cloud, including open-weight model selection, quantization, GPU inference serving, and model registry/versioning.
• Own retrieval-augmented generation design covering embeddings, vector store, hybrid retrieval, reranking, and tool-use patterns that are deterministic and traceable.
• Establish evaluation and model-risk practice, including golden datasets, evaluation harnesses, groundedness metrics, drift detection, and prompt-injection defenses.
• Own CI, CD, and MLOps end-to-end, covering trunk-based development, automated testing, container scanning, progressive delivery, infrastructure as code, model lineage, and drift monitoring.
• Translate business problems into domain models, capability roadmaps, and architecture decisions.

What you'll need

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering or a related field.
• 15+ years of experience in software engineering, including 8+ years in architecture roles and 5+ years leading engineering organizations.
• Demonstrated ownership of a distributed platform architecture at scale, covering domain decomposition, API and event contract management, resilience, 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.
• Fluency in architecture documentation and governance practice: C4 or equivalent modelling, architecture decision records, and design review standards.
• Proven accountability for delivering a platform to committed dates across multiple squads.
• Direct experience architecting and operating CI/CD pipelines and a production MLOps lifecycle.
• Deep hands-on capability across the technology stack, with the credibility to set reference implementations and lead development efforts.
• Practical experience deploying machine learning or LLM-based capability to production, including GPU capacity planning, inference serving, and model optimization.
• Demonstrated ability to elicit and structure requirements from business and customer stakeholders and translate them into domain models and architecture decisions.
• Track record of building a shared platform across business units with competing priorities.
• Semiconductor industry experience in fab operations, equipment software, process control, yield analysis, manufacturing systems or design enablement.

Nice to have

• Direct engagement with leading logic and memory fabs as a software supplier.
• Experience taking an on-premises product to a multi-tenant SaaS delivery model.
• Experience with digital twin, simulation or virtual metrology software in a manufacturing context.
• Familiarity with semiconductor data and equipment integration standards and factory automation interfaces.
• Experience with open-weight model deployment, GPU cluster scheduling and inference optimization frameworks.
• Prior ownership of a reusable component library or internal developer platform.
• Experience establishing AI governance covering model risk management and evaluation standards in a regulated or IP-sensitive enterprise.
• Background in developer enablement at scale, including technical community building and measured adoption of engineering tooling.

Skills: AI platform, Kubernetes, microservices, MLOps, LLM