At a glanceSummarised by Seekless from the posting.
Must have4
Minimum of 10 years of dedicated experience within the cybersecurity domain (e.g., AppSec, Cloud Security, or DevSecOps)
Minimum of 3 years of hands-on experience specifically focused on AI/ML security, LLM vulnerabilities (OWASP Top 10 for LLMs), and guardrail frameworks
Proven experience configuring security policies, IAM, and DLP tools natively within AWS and GCP
Deep understanding of modern CI/CD tools (specifically Harness) and artifact management platforms (Artifactory)
Nice to have2
Strong working experience securing containerized workloads using Docker and managing security policies within Kubernetes (K8s) environments
Proficiency in Python, Go, or similar languages to develop custom AI-driven security scanning utilities
Skills
AWS
GCP
Harness
JFrog Artifactory
Docker
Kubernetes
Python
Go
Legal
TransUnion’s Job Applicant Privacy Notice
Team Overview
At TransUnion, we have a welcoming and energetic environment that encourages collaboration and innovation – we’re consistently exploring new technologies and tools to be agile. This environment gives our people the opportunity to hone current skills and build new capabilities, while discovering their genius.
Come be a part of our team – you’ll work with great people, pioneering products and cutting-edge technology.
This is a hybrid position and involves regular performance of job responsibilities virtually as well as in-person at an assigned TU office location for a minimum of two days a week.
Role Overview And Core Responsibilities
Key Responsibilities
1. AI & LLM Guardrails and Agent Security
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Enterprise Guardrails: Design, implement, and enforce LLM guardrails across user/desktop applications and cloud-hosted environments to prevent prompt injection, data exfiltration, and toxic outputs.
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AI Agent Security: Architect and implement secure execution boundaries, sandboxing, and identity/access management (IAM) frameworks specifically tailored for autonomous cloud-hosted AI agents.
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Threat Modeling: Conduct advanced threat modeling on LLM architectures, RAG (Retrieval-Augmented Generation) pipelines, and agentic workflows.
2. Cloud Security & Data Loss Prevention (DLP)
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Multi-Cloud Engineering: Configure and maintain strict security postures across AWS and GCP environments hosting AI workloads.
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Advanced DLP: Define, implement, and monitor robust Data Loss Prevention (DLP) policies across cloud infrastructures to ensure sensitive enterprise data is not inadvertently ingested or exposed by AI models.
3. DevSecOps & Pipeline Integration
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CI/CD Automation: Integrate automated security scanning directly into enterprise deployment pipelines using orchestration tools like Harness.
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Artifact Security: Manage and secure software packages, model weights, and dependencies within JFrog Artifactory, ensuring comprehensive vulnerability scanning before deployment.
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AI for Security: Design, develop, and implement custom security scanning capabilities powered by machine learning and AI to detect anomalies, code vulnerabilities, and misconfigurations.
Required Knowledge And Experiences
Required Criteria:
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Cybersecurity Foundation: Minimum of 10 years of dedicated experience within the cybersecurity domain (e.g., AppSec, Cloud Security, or DevSecOps).
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AI Security Specialization: Minimum of 3 years of hands-on experience specifically focused on AI/ML security, LLM vulnerabilities (OWASP Top 10 for LLMs), and guardrail frameworks (e.g., Agent Gateway, Model Armor Guardrails, Llama Guard, or other cloud-native equivalents).
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Cloud Proficiency: Proven experience configuring security policies, IAM, and DLP tools natively within AWS and GCP.
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Pipeline Expertise: Deep understanding of modern CI/CD tools (specifically Harness) and artifact management platforms (Artifactory).
Preferred / “Added Advantage” Criteria:
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Containerization & Orchestration: Strong working experience securing containerized workloads using Docker and managing security policies within **Kubernetes (K8s)**environments (e.g., network policies, pod security standards).
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Coding/Scripting: Proficiency in Python, Go, or similar languages to develop custom AI-driven security scanning utilities.
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Certifications: Relevant certifications such as AWS Certified Security, Google Professional Cloud Security Engineer, CISSP, or specialized AI/ML security credentials.