AI Executive Director

We Are

At Synopsys, we drive the innovations that shape the way we live and connect. Our technology is central to the Era of Pervasive Intelligence, from self-driving cars to learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content. Join us to transform the future through continuous technological innovation.

You are a visionary and influential engineering leader who has successfully implemented Artificial Intelligence and Machine Learning to re-engineer workflows, business processes, and operations, with a proven ability to scale AI from early experimentation to organization-wide impact.

You bring strategic vision and technical credibility, excelling at partnering with senior leadership to drive enterprise-wide AI adoption and guide the organization through transformational change. You communicate complex concepts with clarity and impact.

You apply AI to fundamentally redesign business workflows and operations, unlocking new levels of efficiency, predictability, and business growth. You are a builder of platforms, standards, and culture, able to break down complex problems into clear, actionable components. Your familiarity with semiconductor design methodologies and EDA tools helps you recognize opportunities to integrate AI into engineering workflows, for greater efficiency and productivity.

You maintain a constant pulse on the AI marketplace—emerging technologies, competitive trends, platform evolution, and ecosystem dynamics—and translate these insights into actionable strategy and scalable deployment.

You lead a multidisciplinary AI and engineering team that partners across the organization to mentor teams, consult on opportunities, and help design and deploy AI-enabled engineering and business workflows and tools. These initiatives drive maximum business impact and operational excellence. You work cross-functionally to ensure that all AI efforts align with enterprise governance, ethical standards, and responsible AI principles.

• Deep expertise in machine learning and Generative AI, including LLMs and GPT‑based architectures.
• Hands‑on experience building and deploying AI/ML applications at scale for engineering workflows and business processes.
• End‑to‑end experience with AI systems, including data pipelines and MLOps.
• Experience using cloud platforms to pilot, build, and deploy AI solutions at scale (e.g., AWS, Azure, GCP).
• Ability to navigate semiconductor design environments, including digital and analog design methodologies, flows, and EDA tools.
• Exposure applying ML/AI concepts within engineering or EDA‑related workflows is a strong plus.
• Experience with modern AI architectures, such as retrieval‑augmented generation (RAG), vector databases, and embeddings for LLM‑driven engineering tools.
• Understanding of containerized and distributed systems (Docker, Kubernetes) for scalable AI deployments.
• Familiarity with responsible AI and model governance, including model evaluation, safety, bias detection, and compliance frameworks.
• Experience evaluating AI solutions and platforms, including vendor selection and architectural tradeoffs.

• Shaping AI‑enabled workflows across semiconductor design, including enhancing business, operational, and engineering processes for improved efficiency and impact.
• Leading a multidisciplinary team of AI architects, data scientists, software engineers, and business domain experts, all focused on designing and deploying transformative AI solutions for engineering and business functions.
• Conducting experiments to evaluate model performance, identifying areas for improvement, and implementing optimizations for both engineering and business applications.
• Collaborating with cross-functional teams, including engineering, IT, and business units, to develop scalable AI solutions that align with internal business and technical objectives.
• Participating in generative AI platform teams to ensure alignment with application requirements, deployment models, and release timelines for both engineering and business functions.
• Communicating complex technical…