Senior AI Security Automation Engineer

Descriptions & Requirements

Job Description and Requirements

We Are Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. You Are You are a senior security engineer who understands that AI is rapidly changing how enterprises operate, how developers build, and how attackers think. Securing AI means protecting prompts, agents, identities, data flows, model interactions, gateways, RAG pipelines, and the broader ecosystem connecting users, applications, tools, and sensitive enterprise data. You have hands-on experience evaluating and operationalizing emerging AI, GenAI, LLM, agentic AI, and cybersecurity technologies in large enterprise environments. You can take a new AI security technology, assess whether it has real value, build a proof of concept, identify integration challenges, and determine production readiness. Working across cloud AI platforms like Azure AI Foundry, AWS Bedrock, and Microsoft Copilot Studio is familiar territory. You understand how AI agents authenticate, access data, call tools, interact with enterprise systems, and create new security risks when identity, authorization, observability, and guardrails are not properly designed. You conceptualize security ideas, build high-level designs, validate architectures, automate integrations, and own the path to production. You do not stop at recommendations. You build, deploy, integrate, monitor, and improve solutions that help defend against emerging AI-driven threats. At Synopsys, you will help build the foundation for secure AI adoption, enabling teams across the company to leverage AI responsibly while reducing risks from data leakage, prompt injection, agent misuse, insecure integrations, weak identity controls, and unmanaged AI adoption.

Design, deploy, and integrate AI security technologies including LLM gateways, MCP gateways, prompt guardrails, AI firewalls, AI DLP controls, AI Security Posture Management platforms, and agentic AI security solutions Build cybersecurity-focused AI agents and automation workflows that improve risk analysis, executive visibility, security reviews, threat detection, posture management, and operational efficiency Develop high-level solution designs and technical architecture patterns for securing GenAI applications, LLM integrations, RAG pipelines, agentic workflows, and cloud-hosted AI services Own production deployment of AI security solutions from proof of concept and technical evaluation through architecture review, implementation, operational handoff, and continuous improvement Evaluate emerging AI, GenAI, LLM, agentic AI, and cybersecurity technologies including open-source tools, commercial platforms, cloud-native services, and infrastructure automation capabilities Deploy and manage AI security controls across cloud AI platforms such as Azure AI Foundry, AWS Bedrock, Microsoft Copilot Studio, and other enterprise AI environments Integrate identity and access controls for AI agents, workloads, gateways, and services using Microsoft Entra ID, workload identities, Conditional Access, Privileged Identity Management, and identity governance capabilities Design security patterns for agent identity, tool authorization, data access, least privilege, Zero Trust enforcement, and secure agent-to-system communication Integrate prompt guardrails, content safety controls, AI DLP, model governance, logging, monitoring, and policy enforcement into AI applications and enterprise AI platforms Conduct technical proof of concepts, architecture reviews, vendor evaluations, control validations, and technology selection activities for AI security