Highlights

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.

Description

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).

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.

onsemi (Nasdaq: ON) is driving disruptive innovations to help build a better future. With a focus on automotive and industrial end-markets, the company is accelerating change in megatrends such as vehicle electrification and safety, sustainable energy grids, industrial automation, and 5G and cloud infrastructure. With a highly differentiated and innovative product portfolio, onsemi creates intelligent power and sensing technologies that solve the world's most complex challenges and leads the way in creating a safer, cleaner, and smarter world.

More details about our company benefits can be found here:

https://www.onsemi.com/careers/career-benefits

Required Qualifications

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.

Preferred Qualifications

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.

onsemi is excited to share the base salary range for this position is $216,775.00 to $390,195.00 Range exclusive of fringe benefits or potential bonuses. The final pay rate for the successful candidate will depend on geographic location, skills, education, experience, and/or consideration of internal equity of our current team members. We also offer a competitive benefits package. https://www.onsemi.com/site/pdf/Benefits-Summary-USA.pdf

Key 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.