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About This Role
Location OPEN
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Location: New York
Other locations: Anywhere in Country
Salary: Competitive
Date: May 28, 2026
Job description
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Requisition ID: 1713849
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.
Technology — Data and Decision Science — AI Native Engineering
Manager, AI/ML Engineer — Memory Layer \& Knowledge Graph
*Master's preferred (CS or related technical field) • Hybrid • AI Native Engineering*
The opportunity
Our Artificial Intelligence and Data team helps apply cutting\-edge technology and techniques to bring solutions to our clients. As part of that, you'll sit side\-by\-side with clients and diverse teams from EY to create a well\-rounded approach to advising and solving challenging problems, some of which have not been solved before. No two days will be the same, and with constant research and development, you'll find yourself building knowledge that can be applied across a wide range of projects now, and in the future. You'll need to have a passion for continuous learning, stay ahead of the trends, and influence new ways of working so you can position solutions in the most relevant and innovative way for our clients. You can expect heavy client interaction in a fast\-paced environment and the opportunity to develop your own career path for your unique skills and ambitions.
EY is investing significantly in our agentic AI platform, and the memory layer is the engineering foundation of that platform. Without memory, agents are amnesiacs they cannot accumulate context, learn from prior runs, or ground decisions in enterprise knowledge. We are hiring a Manager, AI/ML Engineer to build the memory layer end\-to\-end: the graph databases, ontologies, vector stores, retrieval services, and grounding pipelines that power our cognitive harness and the agents that run on top of it.
This is a hands\-on engineering leadership role. You will architect and build the infrastructure — knowledge graphs, vector indices, hybrid retrieval, ontology services, memory APIs, and the integration glue that ties memory into the harness, agent runtimes, and downstream client solutions. You will work closely with our data\-science leadership on memory representation and retrieval quality, and you will own the engineering delivery and production\-readiness of the memory layer itself.
Your key responsibilities
As a Manager in AI Native Engineering, you will play a pivotal role in delivering the memory infrastructure that underpins EY's agentic AI offerings. You will work with a wide variety of clients to deliver the latest data science and big data technologies. Your teams will design and build scalable solutions that unify, enrich, and derive insights from varied data sources across a broad technology landscape. You will help our clients navigate the complex world of modern data science, analytics, and software engineering. We'll look to you to provide technical guidance and perform technical development tasks to ensure data science solutions are properly engineered and maintained to support the ongoing business needs of our clients — and to build the memory layer that makes those solutions possible.
- Architect and build the memory layer of EY's cognitive harness end\-to\-end graph databases, vector stores, hybrid retrieval services, ontology services, memory APIs, and the integration into agent runtimes.
- Design and implement knowledge graphs and ontologies that support agent grounding vocabulary, schema rules, instances, provenance, and cross\-domain mappings (sector and functional ontologies for Finance, Risk, Tax, Supply Chain, HR).
- Build and operate graph database infrastructure (Neo4j, Spanner Graph, Neptune, TigerGraph, or similar) schema design, ingestion, query optimization, and integration with the broader data platform.
- Engineer the vector and hybrid retrieval stack embeddings pipelines, vector indices (Vertex AI Vector Search, OpenSearch, Pinecone, Weaviate, pgvector), reranking, and lexical\-plus\-dense retrieval services.
- Build memory services for working, episodic, semantic, and procedural memory — including TTL, retention, consolidation, forgetting, and audit\-grade provenance for regulated workloads (SOX, HIPAA, GDPR).
- Implement grounding pipelines that connect agent runtimes to the memory layer with low latency, citation tracking, and hallucination guardrails.
- Lead a team of AI/ML engineers and data engineers set technical standards, run code and design reviews, and mentor on production engineering rigor.
- Partner with data\-science leadership on memory representation, retrieval quality, and evaluation, translating science prototypes into hardened production services.
- Stay abreast of AI and data trends new graph paradigms, embedding models, retrieval techniques, agent frameworks (Google ADK, Bedrock AgentCore, LangGraph) and recommend tools and patterns that fit our clients' existing ecosystems.
- Apply combined business and technical knowledge to develop and execute target memory architectures that enable implementation, monitoring, and ongoing evolution of agentic AI at scale.
Skills and attributes for success
This role will work to deliver tech at speed, innovate at scale, and put humans at the center. You will provide technical guidance and share knowledge with team members with diverse skills and backgrounds. You will consistently deliver quality client services, focusing on more complex, judgmental, and specialized issues surrounding agentic AI, memory infrastructure, and emerging foundation\-model technology. You will demonstrate deep technical capabilities and lead through building.
- Strong AI/ML engineer who builds comfortable owning a memory service from schema to API to deploy, and writing the code that gets it there.
- Deep, hands\-on knowledge of graph databases (Neo4j, Spanner Graph, Neptune, TigerGraph, or Stardog) — schema design, query languages (Cypher, GQL, SPARQL, Gremlin), and operating graph infrastructure in production.
- Strong grasp of ontologies and knowledge representation vocabulary, schema rules, instances, axioms, provenance and how ontologies support grounding and reasoning for AI agents.
- Solid working knowledge of cognitive harness / agent\-runtime architectures memory, tools, policies, evaluation, observability and how memory infrastructure plugs into them.
- Hands\-on experience with vector databases and hybrid retrieval — embeddings, ANN indexes, reranking, query rewriting, and semantic caching.
- Strong software engineering fundamentals Python (and ideally one of Java / Go / TypeScript), API design, testing, CI/CD, containerization, and observability.
- Experience designing and operating production AI systems on at least one major cloud (GCP, AWS, Azure, Databricks) including IAM, network controls, encryption, and responsible\-AI guardrails.
- Track record of leading engineering teams setting technical direction, mentoring, and delivering production systems on time.
- Excellent communication skills able to explain memory and graph concepts to clients, and able to defend engineering decisions to architects, data scientists, and partners.
To qualify for the role you must have
- Master's preferred in Computer Science, Software Engineering, Data Engineering, or a closely related technical field. Bachelor's with strong applied experience also considered.
- 6\+ years of applied AI/ML or data engineering experience, with at least 2 years leading engineering teams or major workstreams.
- Demonstrable production experience with graph databases (Neo4j, Spanner Graph, Neptune, TigerGraph, Stardog, or similar), including schema design and query tuning.
- Hands\-on experience designing and implementing ontologies and knowledge graphs for real systems — not just whiteboard.
- Production experience building retrieval / RAG / memory systems vector indices, hybrid retrieval, embedding pipelines, reranking, and grounding.
- Strong software engineering proficiency in Python and the modern AI/ML stack (PyTorch / TensorFlow, Hugging Face, LangChain / LangGraph, vector databases).
- Practical experience with at least one agent framework (Google ADK, Bedrock AgentCore, LangGraph, AutoGen, OpenAI Agents SDK) and one major cloud AI platform (Vertex AI, Bedrock, Azure AI Foundry, Databricks Mosaic AI).
- Experience operating in a client\-facing or cross\-functional environment and delivering production systems under enterprise constraints.
Ideally, you will also have
- Experience with semantic web standards (RDF, OWL, SHACL, SPARQL) and modern property\-graph approaches in the same codebase.
- Experience designing memory architectures that meet regulated\-industry constraints (SOX, HIPAA, GDPR) retention, audit, lineage, and explainability.
- Experience with neuro\-symbolic patterns combining graphs and ontologies with LLM reasoning for higher\-fidelity grounding.
- Experience with stream\-based ingestion (Kafka, Pub/Sub, Kinesis) and CDC patterns into graph and vector stores.
- Open\-source contributions to graph, retrieval, ontology, or agent\-runtime projects.
- Prior consulting, product, or hyperscaler experience — comfortable in a fast, ambiguous environment with senior stakeholders.
What we look for
We are looking for an engineering leader who genuinely loves building infrastructure someone who sees graphs, ontologies, and memory services not as research artifacts but as production systems that have to run, scale, audit, and evolve. You will thrive here if you want to lead a team, ship to Fortune 500 clients across multiple industries, and treat the memory layer as the most important engineered asset in the agentic AI stack.
What we offer
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. 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. Plus, we offer:
- Continuous learning develop the mindset and skills to navigate whatever comes next.
- Success as defined by you we provide the tools and flexibility to make a meaningful impact, your way.
- Transformative leadership insights, coaching, and confidence to be the leader the world needs.
- Diverse and inclusive culture embraced for who you are, empowered to use your voice to help others find theirs.
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 $125,500 to $230,200\. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $150,700 to $261,600\. 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 $125K-$261K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($193K) sits 8% above the category median. Disclosed range: $125K to $261K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
EY AI Hiring
EY has 8 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, US, Hoboken, NJ, US. Compensation range: $142K - $462K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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