Cisco
Security Engineering
Overview
Join Cisco’s Enterprise AI team, the core group enabling Generative AI powered experiences across Cisco. Our mission is to build secure, scalable AI platforms that empower teams to safely develop, deploy, and operationalize AI-powered solutions.
About Cisco
At Cisco, we're revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds.
Requirements & Eligibility
- Bachelor’s or Master’s degree in Computer Science, Cybersecurity related field or equivalent certifications along with work experience.
- 3+ years of experience in application security, cloud security, or platform security engineering.
- Hands-on experience securing distributed systems or cloud-native applications (Kubernetes, APIs, microservices).
- Experience designing and implementing authentication, authorization, and data protection controls.
- Strong programming/scripting skills (Python, Go, or similar) for building security tooling and automation.
Key Responsibilities
- Design and implement security controls for Generative AI systems, including LLM-based applications, RAG pipelines, and agent-driven workflows.
- Identify and mitigate AI-specific threats such as prompt injection, data exfiltration, insecure tool usage, and model abuse.
- Establish guardrails for prompt handling, input/output validation, and safe tool invocation to ensure secure agent behavior.
- Secure Retrieval-Augmented Generation (RAG) pipelines by enforcing data access controls, source integrity, and retrieval boundary protections.
- Define and enforce model access controls, API protections, and runtime monitoring to prevent misuse and unauthorized access.
- Partner with AI platform and infrastructure teams to integrate security into model deployment pipelines and inference services.
- Conduct threat modeling and security assessments for AI systems, including model lifecycle, data pipelines, and inference endpoints.
- Build automated detection and response mechanisms for AI-specific risks, including anomalous queries and unsafe outputs.
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