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About This Role
Location: McLean Other locations: Primary Location Only Salary: Competitive Date: Oct 2, 2026 Job description
Requisition ID: 1749003 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.
Government and Infrastructure – Technology Consulting - AI & Data – AI Automation Engineer - Manager
From strategy to execution, Government and Infrastructure practice of Ernst & Young provides a full range of consulting and audit services to help our Federal, State, Local and Education clients implement new ideas to help achieve their mission outcomes. We deliver real change and measurable results through our diverse, high-performing teams, quality work at the highest professional standards, operational know-how from across our global organization, and creative and bold ideas that drive innovation. We enable our government clients to achieve their mission of protecting the nation and serving the people; increasing public safety; improving healthcare for our military, veterans, and citizens; delivering essential public services; and helping those in need. EY is ready to help our government build a better working world.
EY delivers unparalleled service in big data, business intelligence, and digital analytics built on a blend of custom-developed methods related to customer analytics, data visualization, and optimization. We leverage best practices and a high degree of business acumen that has been compiled over years of experience to ensure the highest level of execution and satisfaction for our clients. At EY, our methods are not tied to any specific platforms but rather arrived at by analyzing business needs and making sure that the solutions delivered meet all client goals.
The opportunity
You will help our clients navigate the complex world of modern AI, data and analytics. We’ll look to you to provide our clients with a unique business perspective on how data science and analytics can transform and improve their entire organization – starting with key business issues they face. This is a high growth, high visibility area with plenty of opportunities to enhance your skillset and build your career.
The AI & Automation Engineer designs, builds and operates the AI and automation capabilities that allow the team to assess a large legacy application portfolio at a speed and scale that manual analysis cannot achieve. This role converts raw source code, database schemas, configuration and technical documentation into structured, evidence-backed portfolio intelligence — component inventories, dependency graphs, capability maps, technical-debt and risk indicators, and consolidation candidates — that directly drive rationalization and modernization decisions. AI & Automation Engineers work side by side with Application Modernization Engineers, who validate automated findings and feed confirmed patterns back into the pipeline, continuously improving accuracy. The capabilities built here become reusable assets for later modernization, enhancement and operations work across the program.
Your key responsibilities
- Build and operate automated ingestion pipelines for source code repositories, database scripts and schemas, build and deployment configuration, and technical documentation across heterogeneous technology stacks (.NET, Java, ColdFusion, JavaScript, batch and legacy runtimes).
- Develop static-analysis and LLM-assisted extraction to identify application components, technology fingerprints (languages, frameworks, versions and libraries), dependencies, interfaces, data-access patterns and shared database usage.
- Build LLM-based capabilities for code and business-logic summarization, business-capability tagging, functional similarity and duplicate detection to surface consolidation candidates, and technical-debt, security and risk indicators.
- Implement retrieval-augmented generation (RAG) and hybrid (keyword and vector) search over portfolio evidence so analysts and client stakeholders can query the portfolio in natural language with source citations.
- Load extracted entities and relationships into graph and relational data stores to support dependency analysis, change-impact analysis, cluster detection and modernization wave sequencing.
- Build evaluation harnesses — golden datasets, grounding and faithfulness checks, accuracy measures and regression tests for prompts and agents — and track quality metrics over time.
- Design human-in-the-loop review points and capture reviewer decisions, model and prompt versions, and rationale to maintain end-to-end traceability of AI-supported findings.
- Instrument pipelines for observability, throughput and token/cost consumption, and optimize for performance and cost at portfolio scale.
- Package prompts, agents, tools and pipeline components as reusable, documented assets for subsequent modernization, enhancement and operations work.
- Produce technical documentation, runbooks and knowledge-transfer materials for the capabilities you build.
- Lead the AI & Automation team: plan and prioritize work, manage delivery against 90-day milestones and remove blockers.
- Own the architecture of the automated assessment platform, including model and tool selection, data flows, security boundary, scalability and cost.
- Establish responsible-AI governance for the program — model risk management, transparency, auditability and human-review policy — aligned with federal AI guidance and the NIST AI Risk Management Framework.
- Serve as the primary technical point of contact with the client on AI methods; present results and explain methodology to client leadership.
- Coordinate closely with the Application Modernization lead so automated findings and manual validation form a single, consistent evidence base.
- Recruit, mentor and develop engineers, and contribute to the staffing plan as the program scales.
Skills and attributes for success
- Understanding of generative AI models (openAI, Anthropic, open-source LLMs) into web-based products
- Experience working with designers, product managers, AI/ML engineers to create intelligent user experience
- Experience writing tests, participating in code reviews
- Seeking out information to learn about emerging methodologies and technologies
- Clarifying problems by driving to understand the true issue
- Looking for opportunities for improving methods and outcomes
- Collaborating, influencing, and building consensus through constructive relationships and effective listening
- Solving problems by incorporating data into decision making
To qualify for the role, you must have
- Must have a Bachelor's degree in computer science, software engineering, information systems, computer engineering or a related field, or equivalent practical experience.
- Must be eligible to obtain and maintain a Secret Clearance
- 6–10+ years of software, data or ML engineering experience, including architecting and delivering production AI/ML systems, leading teams of five or more engineers, and client-facing delivery in federal or regulated environments
- Must be comfortable working in person as needed in the Washington, DC area
Ideally, you’ll also have
- Static code analysis and parsing (Tree-sitter, Roslyn, JavaParser or abstract syntax tree tooling).
- Graph databases (Azure Cosmos DB Gremlin API, Neo4j) and graph algorithms such as clustering and community detection.
- Agent frameworks (LangGraph, LangChain, Semantic Kernel) and Model Context Protocol (MCP).
- LLM evaluation and observability tooling.
- Azure services including Container Apps, AI Search, Cosmos DB, PostgreSQL, Service Bus, Key Vault, Entra ID and private networking; Azure Government experience.
- Experience with legacy application modernization or application portfolio assessment.
- Microsoft Certified: Azure AI Engineer Associate (AI-102) or comparable certification.
- Prior federal government delivery experience.
Due to the nature of our work in Government and Infrastructure, work may be required to be completed at client, EY and/or contractor sites. Our goal is to assign professionals to projects within a commutable distance of their work location office. In certain circumstances, travel may be required beyond your work location based on client and project needs. Candidates should be willing to travel 20 – 30% or more. 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. Mid-level AI roles across all categories have a median of $193,200. 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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