AI Security Architect

Synopsys•Morrisville, NC

3d•$201,000 - $301,000•Onsite

About The Position

Synopsys is seeking a senior technical leader to define, architect, and secure AI-enabled systems, platforms, and services across the company. The role develops and implements security architectures, standards, and governance frameworks for Artificial Intelligence, Generative AI, Agentic AI, Machine Learning, and Large Language Model ecosystems. The position is based in Morrisville, North Carolina; the description also lists Austin, Texas as a hiring location and indicates that the role is not remote eligible. The architect will establish enterprise AI security patterns and controls across cloud, SaaS, on-premises, and hybrid environments. The role addresses the security of AI models, applications, agents, data, and supporting infrastructure, with a focus on practical protections that enable responsible innovation. The position works with engineering and business stakeholders to assess AI risks, build security into AI lifecycles, and align enterprise practices with established frameworks and emerging regulations. The role is part of the Cybersecurity Architecture & Engineering team, which protects Synopsys intellectual property, sensitive information, and critical business systems. It offers the opportunity to influence enterprise-wide AI strategy and help secure products, platforms, and operations supporting the semiconductor industry.

Requirements

Bachelor's degree in Computer Science, Cybersecurity, Engineering, Information Systems, or a related field.
10 or more years of experience in cybersecurity, security architecture, cloud security, or related disciplines, with a track record of building and securing complex systems.
At least 3 years of hands-on experience securing AI, Generative AI, LLM, or ML systems in production environments.
Deep technical knowledge of AI and ML security, LLM architectures, Agentic AI systems, RAG pipelines, AI model lifecycle security, AI governance frameworks, cloud security architectures, IAM, API security, data security and privacy, secure software development lifecycle, and security monitoring and incident response.
Experience conducting threat modeling, architectural risk assessments, and security design reviews for AI systems, including identifying and mitigating risks such as prompt injection, data poisoning, model theft, and agent abuse.
Demonstrated experience working with executive stakeholders and cross-functional teams to drive security strategy, present risk and mitigation recommendations, and influence decision-making.
Familiarity with Kubernetes and container security, MLOps and ModelOps platforms, AI orchestration frameworks, vector databases, and the Model Context Protocol (MCP).
Experience or familiarity with AI ecosystems including Anthropic, OpenAI, NVIDIA AI, Azure OpenAI, AWS Bedrock, and Google Vertex AI.
Ability to explain complex AI security tradeoffs to executives and discuss technical architectures with AI engineers.
Ability to manage multiple AI security initiatives, including architecture reviews, policy development, and vendor evaluations.
Current knowledge of emerging AI threats, attack techniques, and defensive technologies.
Experience in semiconductor, software, SaaS, or technology industries where intellectual property protection and customer trust are important.

Nice To Haves

Master's degree.
CISSP, CCSP, SABSA, TOGAF, or equivalent certifications.
GenAI security-related certifications.

Responsibilities

Design enterprise AI security architecture patterns and reference architectures for GenAI, Agentic AI, ML, and LLM systems across cloud, SaaS, on-premises, and hybrid environments.
Conduct AI threat modeling, architecture reviews, and security assessments, evaluating risks such as prompt injection, data poisoning, model theft, model inversion, membership inference, and agent abuse.
Define security standards and implementation patterns for agentic AI platforms, orchestration frameworks, MCP ecosystems, LLM gateways, and autonomous workflows, including policy enforcement, monitoring, logging, and action controls.
Establish controls for secure model training, fine-tuning, deployment, and monitoring, and drive AI red-team and adversarial testing strategies.
Design and implement Agentic Identity and Access Management strategies for AI agents, autonomous systems, MCP servers, and AI-enabled applications, including controls for service access, secrets protection, model registries, vector databases, RAG architectures, API security, and data classification.
Lead secure AI and model lifecycle initiatives by defining security controls, architectures, and guardrails; implementing protections against AI-specific threats; and driving red-team testing and governance.
Partner with Product Security, Engineering, Cloud, IT, Legal, Privacy, Data Governance, and Enterprise Architecture teams to establish AI security policies, standards, and governance frameworks aligned with NIST AI RMF, ISO 42001, OWASP LLM Top 10, and emerging regulations.
Research emerging AI threats and defensive technologies, evaluate and implement AI Security Posture Management tools, and design secure sandbox environments for evaluating new AI models, solutions, and agents.
Translate complex AI security risks into architecture recommendations and mitigation priorities for executive and cross-functional stakeholders.
Promote a security-by-design culture and provide reusable standards that enable engineering and product teams to build secure AI solutions.