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About This Role
Location: Atlanta Other locations: Anywhere in Country Salary: Competitive Date: Sep 25, 2026 Job description
Requisition ID: 1747366 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.
The opportunity
We are seeking an AI Security Engineer to own the security posture of EY’s Agentic AI platform end to end.
Agentic AI breaks the assumptions most enterprise security programs are built on. Systems now generate and execute their own code, invoke tools and external APIs autonomously, act on behalf of human principals across long delegation chains, and can be manipulated through the same channel that carries legitimate instructions. Perimeter controls, static code review, and human-in-the-loop approval do not contain any of this on their own.
This role exists to make EY’s agentic platform defensible in the most highly regulated client environments in the world, including tax, financial services, audit, and risk. It is the security engineering and assurance authority spanning the whole stack: from silicon-level attestation and Kubernetes hardening, through supply-chain integrity and sandboxed execution, to prompt-injection defense, agent authority containment, and audit-grade evidence.
This is a deliberately broad mandate. It is ideal for a principal security engineer who has genuine depth in cloud-native and platform security, who has moved decisively into AI and agentic threat models, and who is equally comfortable writing a threat model, breaking a system in a red-team exercise, defining policy-as-code, and defending the resulting engineering to a client’s CISO or a regulator.
Your key responsibilities
- Own the platform threat model: covering agent autonomy, tool invocation, delegated authority, model and data supply chain, multi-tenancy, and every deployment target from cloud to air-gapped, and keep it current as the platform evolves through each build phase.
- Define the security engineering and control set for every platform layer: infrastructure and boot chain, Kubernetes and cluster fabric, identity and secrets, secure execution and sandboxing, gateway and egress, data and state, delivery pipeline, and telemetry.
- Set the secure-by-default contract so that platform capabilities arrive hardened, including agent templates, Helm charts, sandbox profiles, and network policy ship with correct controls rather than requiring teams to add them.
- Own defense against agentic threat classes including direct and indirect prompt injection, jailbreak and instruction hijacking, excessive agency, confused-deputy and authority-escalation attacks, tool and function-call abuse, memory and context poisoning, and retrieval-augmented data exfiltration.
- Work with the architecture team to help define the agent authority model: delegated and on-behalf-of authority, scope and delegation-depth limits, consent boundaries, and the non-escalation invariant that an agent never exceeds the authority of its initiating principal at any hop.
- Own the sandboxing security standard for agent-generated code execution: isolation boundaries, filesystem and credential scope, egress restriction, resource containment, and the escape-test suite that proves the boundary holds.
- Secure the model and knowledge supply chain: model provenance and integrity, upstream registry governance, poisoning and backdoor risk, embedding and vector-store integrity.
- Secure agent-to-agent and tool protocols including MCP and A2A surfaces: discovery trust, tool registration and approval, schema validation, and authorization of inter-agent calls.
- Lead AI red teaming and adversarial testing: build the offensive capability and the recurring exercise cadence that tests guardrails, sandboxes, and authority boundaries before adversaries and auditors do.
- Own supply-chain integrity end to end: artifact signing and verification (Sigstore/Cosign, Notation), SBOM generation and attestation, provenance and SLSA-aligned build integrity, CVE management, dependency and license governance.
- Own admission and runtime policy: policy-as-code across Kyverno and OPA, signature-verification enforcement, Pod Security Standards, and the guardrails that make non-compliant workloads unschedulable rather than merely reported.
- Define Kubernetes and infrastructure hardening baselines: CIS-aligned cluster configuration, network default-deny and segmentation, node and boot-chain integrity, GPU and DPU isolation, and secrets-handling standards.
- Own tenant isolation assurance: the security definition of a tenant boundary across compute, network, storage, secrets, telemetry, and evidence, and the testing that proves cross-tenant leakage is not possible.
- Serve as the security authority in client engagements: lead security engineering reviews, respond to client CISO and regulator scrutiny, and produce the assurance artefacts that unblock deployment into regulated environments.
- Drive security detection and response for the platform: detection engineering for agentic misbehavior, security telemetry requirements, alerting, incident response playbooks, and post-incident review.
Skills and attributes for success
- Deep cloud-native security expertise: Kubernetes, container, and infrastructure security at production scale, with real operational experience rather than assessment-only exposure.
- Genuine command of AI and agentic threat models, with the judgement to distinguish novel risk from familiar risk wearing new vocabulary.
- Strong zero-trust and workload-identity foundations: SPIFFE/SPIRE, PKI and certificate lifecycle, secrets management, and delegated authorization patterns.
- Fluency in policy-as-code, with the instinct to encode controls as enforced policy rather than documented expectation.
- Software supply-chain security depth: signing, provenance, SBOM, and build integrity.
- Offensive-security instinct: able to think like an attacker against systems that generate their own code and act autonomously.
- Pragmatism about risk: able to distinguish controls that must exist before the first client workload from those that can follow, and to defend both decisions.
- Exceptional communication: able to move between a deep technical design review, an executive risk conversation, and a regulator or client CISO discussion without losing precision.
- Security-as-enablement mindset: measured by how much safe delivery velocity the controls unlock, not by how much they prevent.
To qualify you must have
- Bachelor’s or Master’s degree in Computer Science, Security, or a related technical field, or demonstrably equivalent depth.
- 10+ years in security engineering, security engineering, or offensive security, including hands-on production ownership.
- Demonstrable depth in cloud-native and Kubernetes security: admission control, network policy, workload isolation, and runtime security in production.
- Hands-on experience with workload identity and secrets management (SPIFFE/SPIRE, Vault/OpenBao or equivalents) and with PKI and certificate lifecycle.
- Practical experience securing AI or ML systems in production, including familiarity with LLM and agentic attack surfaces, such as prompt injection, tool abuse, excessive agency, and model or data supply-chain risk.
- Threat modelling capability applied to real systems, with evidence that the resulting controls were built and verified.
- A track record delivering under compliance, security, or regulatory constraint with audit-grade evidence requirements.
- Experience defining ownership boundaries and control contracts with platform, data, runtime, and delivery teams.
Ideally, you'll also have
- Software supply-chain security experience: artifact signing, SBOM, provenance, and vulnerability management embedded in delivery pipelines.
- Policy-as-code experience with OPA, Kyverno, or equivalent admission and authorisation engines.
- Experience building or leading an AI red team, or running adversarial testing against LLM and agentic systems.
- Familiarity with confidential computing and hardware attestation (Intel TDX, AMD SEV-SNP, SGX, NVIDIA CC) and secure boot / measured boot designs.
- Working knowledge of the OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, and the EU AI Act as applied to real engineering.
- Experience with LLM guardrail and defense tooling (NeMo Guardrails, LLM Guard, LlamaFirewall, Guardrails AI, or equivalents) in production paths.
- Experience securing multi-tenant platforms across cloud, on-prem, edge, client-managed, and air-gapped deployment modes.
- Detection engineering and incident response experience, particularly for novel or behavioral threat classes.
- Client-facing or consulting background, with credibility in front of CISOs, auditors, and regulators.
- Relevant certifications (CISSP, OSCP, GIAC, cloud security specialties) or demonstrable equivalent depth.
- Exposure to regulated industries: financial services, tax, audit, healthcare, or public sector.
- Contribution to open-source security tooling, research, or public standards work in AI security.
What we offer you
At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.
- 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 is:
- + New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $150,700 to $251,200 + Bay Area California offices – $157,100 to $261,600 + All other offices locations in the US, including Sacramento – $125,500 to $230,200
- Individual salaries within these 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.
- 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.
- To make the most of your application experience, please limit yourself to two applications within a six-month period.
- EY accepts applications for this position on an on-going basis.
- For those living in California, please click here for additional information.
- At EY, our values set the foundation for how we work and the behaviors we expect of our people. Any misrepresentation or falsification of information or lack of integrity at any point in the recruiting process may result in withdrawal of your candidacy, revocation of an offer or immediate termination of employment.
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.
All in to shape the future with confidence.
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 $125K-$261K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($193K) sits 9% below the category median. Disclosed range: $125K to $261K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
EY AI Hiring
EY has 65 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Engineering Manager, AI Architect. Positions span Secaucus, NJ, Sydney, New South Wales, Australia, McLean, VA, US. Compensation range: $142K - $357K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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