AI Executive Director

About the role

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.

Responsibilities

• 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 concepts and findings to senior leadership and to technical and non-technical stakeholders across the organization.
• Leading solution architecture reviews, platform alignment, and deployment strategies for generative AI applications across engineering and corporate operations.
• Driving innovation in new generative AI approaches and staying current on the latest research, with a focus on both enterprise and engineering applications.
• Driving AI culture and mindset transformation across the organization, championing the integration of AI into how teams think, work, and deliver impact.

Requirements

• A bachelor’s degree in engineering, computer science, or a related technical field, or equivalent experience
• 8+ years of proven experience building, scaling, and deploying AI and machine learning systems, with at least 5+ years in a senior leadership role
• Deep expertise in modern AI architectures, including large language models, GPT-based systems, retrieval-augmented generation, and vector-based search architectures
• Strong architectural judgment across cloud-based AI platforms and experience with scalable, containerized deployments
• Excellent communication and presentation skills, capable of translating complex technical concepts for diverse audiences across engineering and business domains.
• A strong foundation in applying AI across engineering-intensive workflows as well as business processes; familiarity with semiconductor design, EDA workflows, or equivalent technical domains is a plus but not required
• Experience integrating AI solutions with corporate and engineering IT infrastructure, including databases, data warehouses, and API management.
• Awareness of data privacy regulations and compliance issues, especially those relevant to corporate and engineering data (e.g., GDPR, CCPA).
• Familiarity with project management methodologies and tools commonly used in both business and engineering settings.
• Proven experience leading cross-functional AI and engineering teams and influencing organizational change

Qualifications

• 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.
• Familiarity with containerized and distributed systems (Docker, Kubernetes) for scalable AI deployments.
• Understanding of 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.

Skills

• Deep expertise in machine learning and Generative AI, including LLMs and GPT-based architectures.
• Experience with cloud platforms (AWS, Azure, GCP).
• Experience with semiconductor design methodologies and EDA tools.
• Experience with large language models, GPT-based systems, retrieval-augmented generation, and vector-based search architectures.
• Experience with containerized and distributed systems (Docker, Kubernetes).
• Experience 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.

Benefits

• Comprehensive health, wellness, and financial benefits as part of a competitive total rewards package.

Pay

The base salary range for this role is across the U.S.

Schedule

Full-time