Director, Product AI Platform Architect

Description

The company makes materials science and engineering equipment and services used in nearly every new semiconductor chip and advanced display. Its tools support AI development and the commercial ramp of next-generation chips.

The team is building one AI platform for several business units, running in an air-gapped environment.

The role owns the platform's architecture and technical delivery. You will work with senior architects and principal AIML Ops engineers, set technical direction, turn business problems into shared platform capability, and stay hands-on in development.

Responsibilities

• Own the target-state reference architecture: domain decomposition, bounded contexts, ports and adapters, and API-first and event-driven boundaries, recorded as C4 views and decision records.
• Design one system that runs on premises in restricted networks, in private cloud, or as a multi-tenant public cloud service, with the hosting choice made by configuration rather than separate code branches.
• Set the Kubernetes and microservices standard: Helm packaging, operator patterns, GitOps reconciliation, service mesh where it earns its place, and the scaling, caching and sharding approach for the hosted version.
• Define the AI architecture for models on customer hardware and in the company cloud: open-weight model selection and quantization, GPU inference serving with continuous batching, and model registry and versioning.
• Own retrieval-augmented generation design: embeddings, vector store, hybrid retrieval, reranking, and tool use that is deterministic, bounded and fully traceable.
• Establish evaluation and model-risk practice: golden datasets, evaluation harnesses, groundedness metrics, drift detection, prompt-injection defenses, and a clear stance on building versus adopting models.
• Own CI, CD and MLOps end to end: trunk-based development, automated testing and quality gates, container scanning and SBOMs, progressive delivery, infrastructure as code, model lineage and drift monitoring.
• Hold teams to the architecture and the schedule. Own release sequencing across squads, dependency and risk management, capacity planning, and the DORA metrics that show delivery health.
• Work with business units and customers to understand their processes, constraints and pain points, then turn them into domain models, capability roadmaps and architecture decisions.
• Act as senior technical contact for product management, field engineering, manufacturing systems, data science, IT and InfoSec. Chair architecture governance and maintain one sequenced roadmap.
• Design the extension model: plugin and adapter interfaces, versioned APIs with backward compatibility, declarative configuration, and tenant isolation across silo, bridge and pool patterns.
• Architect integration with equipment and factory automation interfaces, MES, EDA and yield management systems, historians and data lakes, behind stable anti-corruption layers.
• Own SLOs and error budgets, OpenTelemetry observability, resilience patterns such as circuit breakers and bulkheads, zero-trust segmentation, and data governance for residency, retention and egress.
• Stay credible across the stack (Java and Spring Boot, Python, Node.js,.NET, React, Angular) and lead development directly where risk calls for it.
• Own strangler-fig migration of legacy systems onto the common platform, plus a published deprecation policy and a funded technical debt register.
• Interface with managers and principal engineers across teams and geographic sites.

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering or a related field.
• 15+ years in software engineering, with 8+ years in architecture roles and 5+ years leading engineering organizations that have managers reporting in.
• Proven ownership of a distributed platform at scale: domain decomposition, API and event contracts, resilience and consistency strategy, multi-tenancy and tenant isolation.
• Hands-on architecture work across AWS and/or Azure and Kubernetes or OpenShift, including container packaging, orchestration, GitOps and lifecycle management.
• Experience designing for both customer-controlled on-premises deployments and cloud delivery, weighing the trade-offs and working within customer security governance.
• Fluent in architecture documentation and governance: C4 or equivalent, decision records, design review standards, fitness functions, NFR definition and technical debt management.
• Track record of delivering a platform to committed dates across several squads: release planning, dependency and risk management, capacity planning and quality ownership. Architecture authority without delivery accountability does not meet the bar.
• Direct experience building and running CI/CD pipelines and a production MLOps lifecycle, and using DORA and similar metrics to find and fix delivery problems.
• Deep hands-on skill across the stack, enough to set the reference implementation, review design and code, and lead development directly. The role is not purely advisory.
• Experience running a large multi-stack application portfolio to shared engineering standards and migrating legacy systems onto a shared platform.
• Practical experience putting machine learning or LLM capability into production, including GPU capacity planning, inference serving and optimization for constrained environments.
• Ability to draw out and structure requirements from business and customer stakeholders through process mapping, current and future state analysis, use-case definition and workflow analysis, then turn them into domain models, capability maps and architecture decisions.
• Record of building a shared platform across business units with competing priorities, merging overlapping needs into common capability and keeping them aligned when local incentives pull apart.
• Skill at separating true platform capability from customer or BU-specific customization, and designing configuration and extension models that absorb the difference without forking the codebase.
• Experience building and defending a platform investment case in commercial terms such as engineering cost avoided, customer time to value, deployment cycle time and compliance exposure.
• Comfort in customer-facing technical settings: requirements workshops, design reviews, security assessments and qualification discussions.
• Semiconductor industry experience in fab operations, equipment software, process control, yield analysis, manufacturing systems or design enablement, deep enough to reason about customer process problems, not just software symptoms. This is a hard requirement.
• Competencies: architectural judgment about which decisions are costly to reverse; business translation that gets past the stated request to the underlying need; delivery ownership of dates and quality; technical credibility earned through doing the work; influence without authority over stakeholders who do not report to you; honest use of AI, applied where it measurably helps and set aside where a deterministic method works better.
• Follow the company's Standards of Business Conduct at all times and hold the highest level of ethical conduct, in line with the company's core values.
• Preferred: direct work with leading logic and memory fabs as a software supplier, including security and qualification reviews.
• Preferred: moving an on-premises product to a multi-tenant SaaS model, including tenancy, metering, onboarding and operational changes.
• Preferred: 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: AI governance experience covering model risk management, acceptable use policy and evaluation standards in a regulated or IP-sensitive enterprise.
• Preferred: developer enablement at scale, such as technical community building, hackathons, champion networks and measured tool adoption.
• Preferred: Master's degree.

Conditions

Salary range $218,500.00 to $300,500.00, based on location, hire grade, job-related knowledge, skills, experience and internal equity.

Location: Santa Clara, CA.

Full-time regular assignee role.

Travel: 25% of the time.

Relocation eligible: yes.

For sales roles, the posted range is the Target Total Cash range, meaning base salary plus target bonus at 100% goal achievement.

Accommodation requests go to the company's HR help line or accessibility contact; that channel cannot be used to check application status.

Benefits

• Comprehensive benefits package.
• Possible participation in a bonus and a stock award program, as applicable.
• Programs and support for personal and professional growth, and for wellbeing at work and at home.
• Support to learn, develop and grow your career.