Director, Product AI Platform Architect

What you'll do

• Own the target-state reference architecture using domain-driven decomposition, API-first boundaries, and C4 views.
• Architect a single system capable of running on-premises in restricted networks, in private clouds, and as multi-tenant public cloud services.
• Set the Kubernetes and microservices standards, including Helm packaging, operator patterns, and GitOps reconciliation.
• Define the AI architecture for model selection, quantization, GPU inference serving, and model versioning.
• Design the retrieval-augmented generation (RAG) system covering embeddings, vector stores, and deterministic tool-use patterns.
• Establish AI assurance practices including golden datasets, drift detection, and prompt-injection defenses.
• Own the end-to-end CI/CD and MLOps pipelines, including infrastructure as code and model lineage.
• Translate business problems from various units into domain models, capability roadmaps, and technical architecture decisions.

What we're looking for

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
• Master’s degree (preferred).
• 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/event contracts, resilience, and multi-tenancy.
• Hands-on architecture experience across public cloud (AWS and/or Azure) and Kubernetes or OpenShift.
• Experience architecting for both on-premises customer-controlled deployments and cloud-hosted delivery.
• Fluency in architecture documentation and governance, including C4 modeling, architecture decision records, and technical debt management.
• Proven accountability for delivering a platform to committed dates across multiple squads, including release planning and capacity management.
• Direct experience architecting and operating CI/CD pipelines and a production MLOps lifecycle using metrics like DORA.
• Deep hands-on capability across the technology stack, including Java, Spring Boot, Python, Node.js, .NET, React, and Angular.
• Practical experience deploying machine learning or LLM-based capabilities to production, including GPU capacity planning and inference serving.
• Ability to elicit and structure requirements from business stakeholders and translate them into domain models and capability maps.
• Track record of building a shared platform across business units with competing priorities and converging overlapping requirements.
• Semiconductor industry experience in fab operations, equipment software, process control, yield analysis, or manufacturing systems.
• Direct engagement with leading logic and memory fabs as a software supplier (preferred).
• Experience taking an on-premises product to a multi-tenant SaaS delivery model (preferred).
• Experience with digital twin, simulation, or virtual metrology software in a manufacturing context (preferred).
• Familiarity with semiconductor data and equipment integration standards and factory automation interfaces (preferred).
• Experience with open-weight model deployment, GPU cluster scheduling, and inference optimization frameworks (preferred).
• Prior ownership of a reusable component library or internal developer platform adopted across a large application portfolio (preferred).
• Experience establishing AI governance covering model risk management and evaluation standards in a regulated enterprise (preferred).
• Background in developer enablement at scale, including technical community building and measured adoption of engineering tooling (preferred).