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About This Role
Location: Hoboken Other locations: Anywhere in Country Salary: Competitive Date: Oct 5, 2026 Job description
Requisition ID: 1744992 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.
Today's world is fueled by vast amounts of information. Data is more valuable than ever before. Protecting data and information systems is central to doing business, and everyone in EY Information Security has a critical role to play. Join a global team of over 1000 people who collaborate to support the business of EY by protecting EY and client information assets. Our Information Security professionals enable EY to work securely and deliver secure products and services, as well as detect and quickly respond to security events as they happen. Together, the efforts of our dedicated team help protect the EY brand and build client trust.
Within Information Security we blend risk strategy, digital identity, cyber defense, application security and technology solutions as we consider the entire security lifecycle. You will join a team of hardworking, security-focused individuals dedicated to supporting, protecting and enabling the business through innovative, secure solutions that provide speed to market and business value.
The opportunity
As an Associate Director in Information Security, this position leads the application of AI engineering within a global technology program, working closely with program leadership, workstream leads and technologists from across the globe. The position is responsible for the AI agents and automation that produce the program's reporting to senior leadership and to regional teams, and for the technical standards that govern how the program builds with AI.
This position also serves as the AI leader for Technology Assurance, Risk and Policy, guiding teams across the function on where and how to apply AI and agentic AI within our services and processes. This includes identifying which assurance, risk and policy activities are suited to AI, advising teams on the approach to take, and establishing the practices that allow AI to be adopted consistently and safely across the function.
This is a hands-on engineering position at a senior technical grade, with authority across the program and no direct reports. The position runs and builds AI agents and automation using Claude alongside Python, Power Automate, and the Microsoft platform, and determines what work is given to an agent, how agent output is verified, and what remains with people.
Areas of focus
- AI Engineering: Building agentic workflows in Claude that complete an entire piece of work, from reading source data through to producing the finished output.
- Functional AI Leadership: Acting as the AI leader for Technology Assurance, Risk and Policy, guiding teams on where and how to apply AI and agentic AI within the function's services and processes.
- Automation Delivery: Owning a program's automation estate, including what is automated, how it is built, and how it is supported over time.
- Assurance of AI Output: Accountability for reporting that is accurate, reconciled and defensible, with verification of agent output built into the systems that produce it.
- AI Standards and Capability: Setting the technical direction and standards for how the program builds with AI, and developing engineering capability across the program.
Key responsibilities
Key responsibilities include:
- Design, build and operate the AI agents and automation that produce the program's recurring reporting, using Claude and Claude Code, integrated with the enterprise systems.
- Own the AI and automation roadmap for the program, prioritizing work according to the effort it saves and the risk it reduces.
- Guide teams across Technology Assurance, Risk and Policy on where and how to apply AI and agentic AI within the function's services and processes, including which activities are suited to AI and the approach to take.
- Establish the practices that allow AI to be adopted consistently and safely across the function, and support teams in applying them.
- Establish how AI output is verified, including reconciliation and checks against source data on every run, and define the conditions under which an agent must stop and escalate rather than continue.
- Set the standards the program builds to for AI use, covering how work is scoped to an agent, prompt and context engineering, testing for repeatability, documentation and the handling of sensitive data.
- Advise program leadership on where AI is appropriate, including where a conventional solution is the better answer, and on the cost, risk and effort involved.
- Evaluate developments in AI tooling and models and introduce what is of value to the program.
- Build automation that is resilient and supportable, with error handling, secure credential management, logging and alerting, held in version control and documented so that it can be maintained by others.
- Work with IT and Information Security to secure approval, connectivity and ongoing support for AI tooling, and build within firm policy on AI use, data classification and client confidentiality.
- Represent the program's AI and automation position to senior stakeholders and governance forums.
- Develop AI engineering capability across the program through reusable components, documented patterns and practical working sessions.
Skills and attributes for success
- Proven technical authority in AI engineering, built on agents and automation in production that other people depend on.
- Ability to judge where AI is the right tool, where a conventional solution is better, and what should remain manual.
- Understanding of the limitations of AI models, and the ability to design verification appropriate to those limitations.
- Strong analytical skills, with the ability to validate the accuracy of reporting relied on by senior leadership.
- Capable of building relationships with senior stakeholders.
- Proficient in explaining technical trade-offs and their business implications to a non-technical audience.
- Ability to work independently and set technical priorities in a large program.
- Ability to team well with others and raise the AI engineering capability of colleagues.
- Strong organizational skills and the ability to deliver against fixed reporting deadlines.
- Ability to maintain a high level of integrity, trustworthiness and confidence when handling confidential information.
To qualify for the role, you must have
- At least 10 years of experience in software, automation or data engineering, including significant time as a senior individual contributor with technical ownership of systems in production.
- Demonstrable experience building production solutions with Claude, including Claude Code and the Claude Agent SDK.
- Experience designing agentic workflows that complete an end-to-end task.
- Practical prompt and context engineering, including testing that model output is reliable and repeatable.
- Strong Python, including automation of document and data workflows with libraries such as python-pptx, python-docx, openpyxl and pandas, or the equivalent.
- Microsoft platform experience: Power Apps, Power BI, Dataverse, Copilot Studio and Microsoft Graph.
- Production experience with a major enterprise workflow automation platform such as Power Automate, including error handling and unattended runs.
- A track record of automating a recurring operational or reporting process end to end, with demonstrable outcomes.
- Experience building unattended systems that run reliably, including error handling, credential management, logging, alerting and scheduling.
- Experience working with large datasets drawn from multiple systems, including reconciling them and assessing whether the data supports the conclusions being drawn from it.
- Experience setting technical standards that other people build to, through review, shared components or documented patterns.
- A degree in Computer Science, Engineering, Information Systems or a related discipline, or equivalent work experience.
- Strong English language communication skills and the ability to work with diverse, globally distributed teams.
Ideally, you'll also have
- Experience integrating AI tooling with enterprise systems and data sources.
- Experience introducing AI tooling into a governed enterprise environment, including the approval and assurance path.
- Experience evaluating models or agent frameworks and making an adoption recommendation.
- Experience delivering security, technology risk or audit reporting to a senior audience.
- Familiarity with vulnerability management data and tooling.
- Experience coaching or building technical capability in colleagues who are not engineers.
- Relevant certification in cloud, data or security engineering.
What we look for
We are looking for individuals with a passion for applied AI and a demonstrated ability to build with it to a standard that a global professional services firm can rely on. This is a senior hands-on position for someone who wants to build AI into the way a large program works and to set the standard for how it is used.
What we offer you
The compensation ranges below are provided in order to comply with United States pay transparency laws. Other geographies will follow their local salary guidelines, which may not be a direct conversion of published US salary ranges. 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 – $188,600 to $339,400 + Bay Area California offices – $196,400 to $353,600 + All other offices locations in the US, including Sacramento – $157,100 to $311,100
- 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 $188K-$353K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($271K) sits 28% above the category median. Disclosed range: $188K to $353K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
EY AI Hiring
EY has 70 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Architect, AI Safety. Positions span Stamford, CT, Hoboken, NJ, New Brunswick, NJ. Compensation range: $142K - $374K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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