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About This Role
Summary:
VIAVI (NASDAQ: VIAV) is a global provider of network test, monitoring and assurance solutions for telecommunications, cloud, enterprises, first responders, military, aerospace, and railway. VIAVI is also a leader in light management technologies for 3D sensing, anti\-counterfeiting, consumer electronics, industrial, automotive, government and aerospace applications.
We are the people behind the products that help keep the world connected at home, school, work, at play, and everywhere in between. VIAVI employees are passionate about supporting customer success and we welcome people who bring their best every day to the company – to question, to collaborate and to push for solutions that will delight our customers.
Senior AI Security and Governance Engineer
Job Summary:
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.Duties \& Responsibilities:
Key Responsibilities:
AI Compliance \& Governance
- 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.
AI Security, Secure Product Development \& Operational Safeguards
- 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.
Data Security for AI
- 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.
Pre\-Requisites / Skills / Experience Requirements:
Required Qualifications:
- 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.
If you have what it takes to push boundaries and seize opportunities, apply to join our team today.
VIAVI Solutions is an equal opportunity and affirmative action employer – minorities/females/veterans/persons with disabilities.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At VIAVI Solutions, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
VIAVI Solutions AI Hiring
VIAVI Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chandler, AZ, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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