Senior AI Security and Governance Engineer

VIAVI Solutions•Chandler, AZ

About The Position

VIAVI Solutions is seeking a Senior AI Security & Governance Engineer to lead the company's strategy for securing AI technologies across the enterprise. As AI adoption accelerates across VIAVI's products and corporate environments, this role serves as the primary authority on three interconnected pillars: AI compliance and governance, AI security and secure product development, and data security. The engineer will drive discovery and management of AI usage, establish enterprise-wide guardrails, and protect sensitive data across AI workflows from model inputs and outputs to inter-agent communication and third-party integrations. This role works in close partnership with Engineering, Legal, Privacy, Product, and Risk teams, and reports to the CISO.

Requirements

Minimum of a Bachelor’s (Preferred Master’s); preferably Computer Science/Computer Engineering or a related field
8–12+ years in security architecture, application security, cloud security, or a closely related field.
3+ years of hands-on experience securing AI/ML or LLM-based systems in enterprise environments, including practical knowledge of prompt injection, data exfiltration through AI APIs, and agentic risk.
Demonstrated experience defining and implementing AI governance frameworks (OWASP Top 10 for LLM, NIST AI RMF, ISO/IEC 42001, EU AI Act, or equivalent).
Strong background in threat modeling, secure design review, and risk management across complex distributed systems.
Hands-on experience with data loss prevention (DLP), CASB, SWG, or equivalent technologies applied to AI and SaaS environments.
Experience designing and enforcing granular access control frameworks (RBAC, ABAC) for AI agents, tools, and data pipelines.
Strong written and verbal communication skills including executive-level reporting and the ability to translate complex AI risk into business language.
Ability to read and review code (Python, JavaScript/TypeScript, or similar) to understand AI workflows, APIs, and failure modes.

Responsibilities

Define and own VIAVI's enterprise AI governance framework, translating policy into enforceable technical controls aligned with NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
Establish and maintain an AI risk tiering and classification system covering data sensitivity, model risk, autonomy level, and business exposure.
Collaborate with IT, Procurement, and Legal to operationalize an AI tool approval and onboarding process.
Build and operate a continuous AI discovery program to identify unsanctioned AI tools, embedded AI features in approved SaaS applications, and browser-based AI interactions across the enterprise.
Build and operate AI intake workflows to evaluate, approve, and track all new AI use cases, tools, models, and integrations before production deployment.
Partner with Legal, Privacy, and Compliance teams to define AI exception and waiver processes; support internal audits and regulatory examinations.
Stay ahead of emerging AI regulations and industry standards including sector-specific requirements and translate them into actionable policy and controls.
Lead AI-specific threat modeling across the full AI lifecycle covering prompt injection, data leakage, model poisoning, adversarial attacks, tool abuse, privilege escalation, and agentic supply-chain risks.
Define and enforce secure AI architecture patterns and prohibited design anti-patterns for LLM-powered applications, autonomous agents, and multi-agent workflows.
Partner with product and platform engineering teams to embed security controls natively into AI development pipelines (S-SDLC / Secure AI Development Lifecycle), including secure CI/CD gates, pre-production reviews, and post-deployment monitoring.
Design and operationalize runtime protections for AI systems including prompt injection detection, jailbreak protection, output content controls, and abuse detection for high-throughput environments.
Define Human-on-the-Loop (HOTL) review checkpoints for autonomous agentic workflows where high-risk decisions require human oversight before execution.
Design and enforce granular data access controls for AI systems, ensuring least-privilege access to tools, data sources, APIs, and enterprise platforms invoked by AI agents; enforce clear segregation of duties across agent orchestration layers.
Implement data usage monitoring across AI workflows to detect unauthorized data access, over-permissioned AI agents, sensitive data exposure in model inputs/outputs, and policy violations in near-real time.
Develop and operationalize controls to prevent data exfiltration through AI channels, including prompt-based exfiltration via LLMs, data leakage through RAG retrieval pipelines, and output exfiltration through API integrations and third-party AI services.
Establish AI-specific data classification policies and enforce data boundary controls, retention limits, and usage constraints for data ingested by or generated by AI systems.