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About This Role
Location: Dallas
Other locations: Anywhere in Country
Salary: Competitive
Date: Jul 31, 2026
Job description
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Requisition ID: 1731223
Location: Anywhere in Country
At EY, we’re all in to shape your future with confidence.
We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.
Securing Frontier AI, Agentic Systems \& AI\-Driven Cyber Defense
Job Family: Cybersecurity \| Level: Senior Manager / Principal \| Location: Hybrid / Remote\-Eligible \| Travel: Up to 30%
ROLE OVERVIEW
The AI Cybersecurity Architect \& Engineer is a hybrid technical leadership role responsible for designing, building, and defending AI systems as first\-class assets — while simultaneously leveraging AI to deliver next\-generation cyber defense capabilities. This individual sits at the intersection of cybersecurity engineering, machine learning, and agentic AI, protecting models, data pipelines, prompts, agents, integrations, and the infrastructure that hosts them, while also using AI offensively (from a defensive standpoint) to match the speed and sophistication of Frontier AI\-enabled attacks.
This role is critical as organizations face a fundamental shift in the threat landscape: Frontier AI models can now autonomously discover and weaponize zero\-day vulnerabilities in hours, AI\-generated polymorphic malware evades signature\-based detection, and autonomous AI worms spread laterally adapting tactics per\-target. Defenders need an equally capable AI\-augmented security posture — built and operated by professionals who understand both domains deeply.
KEY RESPONSIBILITIES
A. AI Security Architecture \& Design
- Design end\-to\-end secure architectures for AI/ML systems, including LLM applications, agentic AI workflows, RAG pipelines, vector databases, and model serving infrastructure.
- Establish threat models for AI systems using MITRE ATLAS, OWASP Top 10 for LLM Applications, OWASP Agentic Security Initiative (ASI) Top 10, and CSA MAESTRO 7\-layer framework.
- Define secure\-by\-design patterns for AI agent permissions, tool invocation, memory/context isolation, and inter\-agent communication (Model Context Protocol, Agent2Agent).
- Architect zero\-trust controls for AI training pipelines, model registries, embedding stores, and prompt template repositories.
- Develop reference architectures for integrating GenAI capabilities into enterprise security platforms (CrowdStrike NGSIEM, Microsoft Sentinel, ServiceNow SIR / Now Assist, Splunk).
B. AI\-Driven Cyber Defense Engineering
- Build and operationalize AI\-augmented detection, triage, and response capabilities — including LLM\-assisted alert enrichment, autonomous incident summarization, and AI\-driven threat hunting.
- Engineer agentic SOC workflows that integrate ServiceNow AI Agents, NVIDIA NIM, OpenAI / Anthropic models, and custom ML models with SIEM / SOAR / XDR platforms.
- Develop detections for AI\-specific TTPs: prompt injection, jailbreak, model extraction, training data poisoning, agent hijacking, MCP server tampering, and rogue agent behavior.
- Build behavioral baselines and anomaly detection for AI agent activity, API call patterns, and inter\-agent communications.
- Design and deploy AI\-specific deception (decoy MCP servers, poisoned data traps, decoy LLM\-accessible resources).
C. Threat Defense Against Frontier AI Attacks
- Lead red\-team and adversarial testing of AI systems — prompt injection, jailbreaks, model extraction, membership inference, training data extraction, and adversarial examples.
- Defend against AI\-generated polymorphic malware, deepfake vishing / social engineering, AI\-orchestrated multi\-stage attacks, and autonomous AI worms.
- Build defenses against AI\-driven vulnerability discovery — including reachability / exploitability validation, compensating controls frameworks, and AI\-speed patching workflows.
- Develop containment playbooks for rogue AI agents, compromised LLM integrations, and AI\-driven cascading failures.
- Operate within the MLSecOps lifecycle: threat modeling, supply chain defense, model assurance, runtime defense, observability / IR, and governance.
D. Governance, Risk \& Compliance for AI
- Implement AI governance frameworks (NIST AI RMF, ISO/IEC 42001, EU AI Act readiness) for enterprise AI deployments.
- Build AI risk assessment methodologies — data provenance, model lineage, prompt template management, third\-party AI vendor risk.
- Establish AI usage policies, Shadow AI detection capabilities, and DLP controls for GenAI tool usage.
- Partner with privacy, legal, and compliance teams on AI\-specific regulatory requirements.
- Define and track AI security KPIs: prompt injection block rate, AI\-generated phishing detection rate, agent permission drift, model integrity validation.
E. Client Advisory \& Practice Leadership
- Lead Frontier AI readiness assessments for client organizations across SOC, incident response, vulnerability management, identity, and data protection.
- Develop client\-facing deliverables: solution architectures, executive summaries, technical roadmaps, and one\-pagers.
- Contribute to thought leadership: whitepapers, client workshops, industry conference presentations.
- Mentor junior engineers and consultants on AI security topics; build internal AI security competency.
- Partner with alliance teams (ServiceNow, CrowdStrike, Microsoft, NVIDIA, OpenAI / Anthropic) on joint solutions and go\-to\-market motions.
REQUIRED QUALIFICATIONS
Experience
- 8\+ years of progressive experience in cybersecurity engineering or architecture.
- 3\+ years of hands\-on experience with AI / ML systems — either building, securing, or red\-teaming.
- Demonstrated experience designing secure architectures for production AI workloads (LLM applications, agentic systems, ML pipelines).
- Proven track record leading complex security projects in regulated industries (financial services, healthcare, energy, government).
Technical Skills — Cybersecurity Core
- Deep expertise in security architecture: zero trust, defense in depth, least privilege, secure SDLC.
- Strong working knowledge of SIEM / SOAR / XDR platforms (CrowdStrike Falcon / NGSIEM, Microsoft Sentinel, Splunk, ServiceNow SecOps).
- Hands\-on experience with cloud security (AWS, Azure, GCP) — IAM, network controls, container / Kubernetes security, secrets management.
- Familiarity with detection engineering (Detection\-as\-Code, Sigma, KQL, EQL, YARA).
- Incident response and forensics fundamentals; comfort with MITRE ATT\&CK and MITRE ATLAS frameworks.
Technical Skills — AI / ML
- Working knowledge of neural network architectures (transformers, CNNs, RNNs), training / optimization, inference / deployment.
- Hands\-on experience with LLM application development (prompt engineering, RAG, fine\-tuning, function calling).
- Experience with AI orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, AutoGen) and agentic patterns.
- Understanding of vector databases (Pinecone, Chroma, Weaviate), embedding models, and inference infrastructure.
- Familiarity with model serving platforms (NVIDIA NIM, Triton, TorchServe, vLLM).
- Proficiency in Python; familiarity with PyTorch and/or TensorFlow.
Technical Skills — AI Security Specific
- Strong understanding of OWASP Top 10 for LLM Applications and OWASP Agentic Security Initiative threats.
- Hands\-on experience with MITRE ATLAS adversarial techniques.
- Knowledge of prompt injection (direct, indirect, triggered), jailbreaks, model extraction, training data poisoning, and adversarial examples.
- Experience with AI red\-teaming tools (e.g., Garak, PyRIT, promptfoo) and LLM security testing methodologies.
- Understanding of differential privacy, federated learning, and secure multi\-party computation (preferred).
PREFERRED QUALIFICATIONS
- Master's degree in Computer Science, Cybersecurity, AI / ML, or a related field.
- Experience implementing or auditing AI governance (NIST AI RMF, ISO/IEC 42001, EU AI Act).
- Contributions to OWASP GenAI Security Project, MITRE ATLAS, MLSecOps Community, or similar.
- Published research, conference talks (RSA, Black Hat, DEF CON AI Village, BSides), or thought leadership in AI security.
- Experience with consulting / client advisory engagements.
- Familiarity with EY's Cyber Practice methodologies, ServiceNow alliance solutions, and CrowdStrike / Microsoft Security alliance offerings.
CERTIFICATIONS (ONE OR MORE PREFERRED)
- CISSP, CISSP\-ISSAP, or CCSP.
- AWS Certified Security – Specialty, Azure Security Engineer (AZ\-500\), or Google Professional Cloud Security Engineer.
- AI / ML\-specific: Certified AI Security Professional (CAISP), AI Cybersecurity Specialist certification.
- MLOps / MLSecOps certifications.
- Vendor: CrowdStrike Certified Falcon Administrator / Responder, Microsoft Security Operations Analyst (SC\-200\), ServiceNow Certified Implementation Specialist – Security Operations.
SOFT SKILLS \& ATTRIBUTES
- Translates complex AI security concepts into business\-impact language for CISOs and boards.
- Comfortable working in ambiguity at the leading edge of an emerging discipline.
- Strong written communication for client deliverables (slide decks, executive summaries, architecture diagrams).
- Collaborative orientation across security, data science, ML engineering, and business stakeholder teams.
- Continuous learner — actively tracks emerging Frontier AI threats and defensive research.
WHAT SUCCESS LOOKS LIKE (FIRST 12 MONTHS)
- Designed and delivered 2–3 production\-grade AI security architectures for enterprise clients.
- Established baseline AI security controls (prompt injection defense, agent permission framework, AI inventory) for internal practice or major client.
- Built or extended a Frontier AI readiness assessment methodology used across the practice.
- Published or presented at least one piece of external thought leadership on AI cybersecurity.
- Mentored 3–5 junior team members on AI security capabilities.
COMPENSATION \& CAREER PATH
- Salary range (US, role\-dependent): $185K – $285K base \+ bonus \+ equity / profit share (varies by level and geography).
Career trajectory: Senior Manager Director / Principal* Partner / Distinguished Engineer / CISO advisory role.
- Continuous education budget for AI / ML conferences, certifications, and applied research.
EQUAL OPPORTUNITY STATEMENT
We are an equal opportunity employer committed to building a diverse, inclusive, and equitable workplace. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected characteristic.
What we offer you
At EY, we’ll develop you with future\-focused skills and equip you with world\-class experiences. We’ll empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams. Learn more.
- We offer a comprehensive compensation and benefits package where you’ll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job in all geographic locations in the US is $82,500 to $136,000\. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $99,100 to $154,600\. Individual salaries within those ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
- Join us in our team\-led and leader\-enabled hybrid model. Our expectation is for most people in external, client serving roles to work together in person 40\-60% of the time over the course of an engagement, project or year.
- Under our flexible vacation policy, you’ll decide how much vacation time you need based on your own personal circumstances. You’ll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well\-being.
Are you ready to shape your future with confidence? Apply today.
EY accepts applications for this position on an on\-going basis.
For those living in California, please click here for additional information.
EY focuses on high\-ethical standards and integrity among its employees and expects all candidates to demonstrate these qualities.
EY \| Building a better working world
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.
Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi\-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.
EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law.
EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1\-800\-EY\-HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY’s Talent Shared Services Team (TSS) or email the TSS at [email protected].
Salary Context
This $82K-$285K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At EY, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($183K) sits 14% below the category median. Disclosed range: $82K to $285K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
EY AI Hiring
EY has 17 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, Data Engineer. Positions span Chicago, IL, US, New York, NY, US, Hoboken, NJ, US. Compensation range: $142K - $390K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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