Senior Director, AI NPD Platforms & Engineering Intelligence

onsemi•San Jose, CA

5d•$216,775 - $390,195

About The Position

onsemi is seeking an industry leading AI and semiconductor technology executive to define, build, and govern the next generation of AI-enabled New Product Development (NPD). This leader will establish the vision, strategy, operating model, and governance framework for Agentic AI across the semiconductor design lifecycle, enabling AI-assisted and AI-driven development of analog, mixed-signal, power management, sensing, memory, verification, layout, test, packaging, and system architectures. The role will serve as the executive authority for Agentic AI Design Engineering and will lead the transformation of semiconductor development workflows through reusable AI agents, advanced reasoning systems, design knowledge repositories, and AI-enabled engineering methodologies. Success in this role will materially improve engineering productivity, accelerate product development cycles, increase IP reuse, enhance design quality, and establish onsemi as an industry leader in AI-enabled semiconductor development.

Requirements

Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, or related technical discipline.
15+ years of semiconductor industry experience.
10+ years of leadership experience in product development, EDA, design methodology, CAD, semiconductor software, or AI-enabled engineering environments.
Deep understanding of semiconductor development workflows including: Analog design, Digital design, Verification, Physical design, Design automation, Semiconductor manufacturing flows.
Experience leading complex cross-functional engineering organizations.
Proven ability to influence executive leadership and drive organizational transformation.

Nice To Haves

Advanced degree (MS or PhD) in Electrical Engineering, AI, Machine Learning, Computer Science, or related field.
Experience developing AI-enabled EDA tools, semiconductor design automation platforms, or Agentic AI systems.
Hands-on experience with: LLMs, Retrieval-Augmented Generation (RAG), Multi-Agent Architectures, Model Context Protocol (MCP), Workflow Orchestration Platforms, Reinforcement Learning, AI Copilots, Knowledge Graphs.
Background in organizational AI transformation programs.

Responsibilities

Define Enterprise AI NPD Vision: Develop and execute the long term strategy for Agentic AI-enabled semiconductor product development. Create the roadmap for AI deployment across product planning, architecture, design, verification, physical implementation, characterization, test, reliability, applications, and documentation. Define the maturity model for AI-assisted, AI-enabled, and AI-native development methodologies. Establish objectives, success metrics, and business outcomes tied to engineering productivity and NPD execution.
Build the Agentic Design Platform: Lead creation of an enterprise Agentic AI framework supporting: Analog design agents, Digital design agents, Verification agents, Layout agents, Test engineering agents, Applications engineering agents, Documentation and requirements agents, Program management agents. Define standards for agent architecture, interoperability, orchestration, security, and lifecycle management. Partner with engineering, CAD, IT, and infrastructure teams to deploy scalable AI design environments.
Govern AI Agents as Strategic IP: Establish companywide governance for AI agents and engineering knowledge assets. Develop processes for agent qualification, validation, release, version control, maintenance, retirement, and reuse. Create an "Agent Library" and corporate repository of approved agents, prompts, workflows, and design knowledge. Define review and approval processes for new internally developed agents. Ensure cross division reuse of agents.
Transform Analog and Digital Design: Define AI-enabled methodologies across: Circuit design, Architecture exploration, RTL development, Verification, Physical design, Device characterization, Design reviews, Failure analysis, Documentation generation. Drive deployment of AI workflows into production engineering environments. Identify high-value use cases capable of delivering significant improvements in development cycle time, engineering efficiency, and design quality.
Create the AI Governance Operating Model: Define AI governance policies for engineering workflows. Establish standards for: Model selection, Training data usage, Intellectual property protection, Security and access controls, Human-in-the-loop decision processes, Validation requirements, Auditability and traceability. Ensure responsible deployment of AI systems across engineering organizations.
Drive Measurable Business Outcomes: Establish KPI frameworks measuring: NPD cycle time, Design productivity, Engineering efficiency, Agent reuse rates, AI adoption, Design quality improvements, Verification coverage improvements, IP creation velocity. Deliver measurable improvements in development throughput and engineering effectiveness.