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About This Role
Location: Tampa
Other locations: Primary Location Only
Salary: Competitive
Date: Jul 16, 2026
Job description
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Requisition ID: 1727008
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
Leads the delivery of solution or infrastructure development services for large or complex AI/ML initiatives, applying strong technical capability and hands\-on engineering experience. Takes accountability for the design, development, delivery, and maintenance of AI\-enabled solutions or infrastructure, while ensuring compliance with and contribution to relevant engineering standards. Understands business and user requirements and translates them into design specifications that are effective from both business and technical perspectives. Owns the implementation and integration of AI/ML capabilities into broader enterprise solutions, with a focus on reliability, scalability, user impact, and successful project delivery.
Your key responsibilities
- Manage design, development, testing, deployment, and support for production\-grade AI/ML, generative AI, and intelligent automation solutions.
- Manage complex technical problems through coding, debugging, testing, troubleshooting, and structured design remediation.
- Manage build and integration of LLM, RAG, and agentic solution components into enterprise applications and platforms.
- Contribute to system design across service boundaries, orchestration layers, data flows, security controls, and external integrations.
- Lead workstreams or project delivery responsibilities through planning, coordination, execution oversight, issue management, and stakeholder communication.
- Drive engineering quality through strong coding standards, CI/CD practices, automated testing, observability, and documentation.
- Partner with Development, Engineering, Product, Data, Architecture, and engagement leadership teams to deliver high\-value AI capabilities.
- Improve performance, resilience, maintainability, and cost efficiency of deployed AI systems.
- Participate in architecture and design reviews, providing thoughtful trade\-off analysis and implementation guidance.
- Use modern AI\-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms as part of delivery leadership and engineering execution.
AI and Engineering Skills:
Gen AI Foundational:
- Ability to understand complex technical business challenges across banking, capital markets, insurance, and asset management and translate them into LLM\-powered solutions that deliver measurable business value
- Practical experience leading and managing multi\-disciplinary teams through the full AI product lifecycle — requirements, architecture, build, evaluation, and production handoff
- Demonstrated experience managing and mentoring teams of AI engineers and data scientists through the execution of specific business use cases, ensuring technical quality and delivery consistency across engagements
- Advanced hands\-on software engineering proficiency in Python, with the credibility to guide implementation decisions as well as architecture across delivery teams
- Demonstrated experience architecting and delivering production\-grade LLM applications including retrieval\-augmented systems, agentic orchestration layers, and structured output pipelines at enterprise scale (e.g. LlamaIndex, LangChain, Azure OpenAI, AWS Bedrock)
- Strong knowledge of embedding models, vector search, semantic retrieval, and NLP similarity systems used in enterprise RAG and knowledge AI architectures (e.g. OpenAI Embeddings, Cohere Embed, Azure AI Search, FAISS etc.)
Agentic and LLM Ops:
- Deep expertise in LLM Ops practices including model lifecycle management, versioning, CI/CD for AI systems, deployment governance, and continuous improvement loops in production environments (e.g. MLflow, Azure ML, GitHub Actions, Kubeflow etc.)
- Execute on agentic system architecture including multi\-agent orchestration, tool use patterns, memory design, and human\-in\-the\-loop workflows for high\-stakes production environments (e.g. LangGraph, AutoGen, Semantic Kernel, CrewAI, NVIDIA NIM etc.)
- Experience governing agent behavior in production environments including audit trail design, cost and latency controls, and reliability management across complex multi\-agent pipelines
- Demonstrated exploration of new LLM techniques and emerging agentic patterns, with the ability to assess their applicability to client challenges and translate them into practical delivery approaches
- Experience defining and governing LLM evaluation frameworks across teams and engagements, ensuring consistent measurement of output quality, safety, and alignment with business requirements (e.g. RAGAS, DeepEval, Arize, Weights \& Biases etc.)
- Ability to drive performance, resilience, maintainability, and cost efficiency improvements in deployed LLM and agentic systems, including post\-deployment optimization and operational tuning
Software Engineering:
- Knowledge of MLOps practices for continuous integration and continuous deployment of AI systems in cloud environments, including containerization and orchestration for scalable and secure LLM deployment (Azure DevOps, GitHub Actions, Kubeflow, MLFlow etc.)
- Experience governing API design standards for LLM and agentic systems including contract design, versioning, error handling, retry semantics, and decoupling of AI service consumers from internal model and workflow topology
- Strong system design capability across service boundaries, asynchronous workflows, data contracts, cloud\-native patterns, and secure deployment models for AI\-enabled applications
- Proficiency in containerization and orchestration for deploying and managing scalable LLM applications in production cloud environments (e.g. Docker, Kubernetes, Azure Container Apps, AWS ECS etc.)
- Ability to collaborate with data engineers, ML engineers, and business stakeholders to align LLM solution design with enterprise data and technology constraints
. To qualify for the role you must have
- A bachelor's or master’s degree
- Minimum of 6 years of applied engineering experience, including significant experience in AI/ML engineering roles.
- Clear communicator able to explain complex AI system behavior and trade‑offs to technical and non‑technical stakeholders, including risk and compliance.
- Strong ownership and accountability, taking responsibility for AI systems from design through production and issue resolution.
- Collaborative and cross‑functional, working closely with engineering, product, risk, legal, and audit teams.
Ideally, you’ll also have
- Experience advising clients on AI platform and infrastructure strategy including model access layer selection, build\-vs\-buy decisions, and integration with existing data and technology infrastructure (e.g. Azure OpenAI, AWS Bedrock, Google Vertex AI, NVIDIA AI Enterprise, Hugging Face etc.)
- Ability to quantify business improvement resulting from LLM solutions through defined evaluation metrics, performance benchmarks, and client\-facing reporting
- Strong ability to design and govern model observability and monitoring strategies across engagements, covering output quality, behavioral drift, and multi\-step agentic workflow tracing (e.g. LangSmith, Arize, Datadog, Azure Monitor etc.)
- Understanding of LLM fine\-tuning methodologies and the ability to advise clients on when and how to apply them, including data preparation, training approaches, and post\-training evaluation (e.g. LoRA, QLoRA, PEFT, NeMo Framework etc.)
- Experience leading controlled model rollout programs including shadow deployment, A/B testing, canary releases, and stakeholder sign\-off processes with defined rollback criteria
- Familiarity with AI security risks specific to LLM systems including prompt injection, data poisoning, and model extraction, and the ability to advise on mitigation and audit trail requirements
- Familiarity with bias, fairness, and explainability approaches and their application in financial services AI systems
- Familiarity with system design principles for AI — scalability, fault tolerance, and distributed architecture for production AI workloads
- Familiarity with data pipeline architecture for enterprise AI workloads including ingestion, transformation, and governance
- Understanding of data security and privacy best practices in cloud environments as they apply to LLM application development and deployment
- Familiarity with AI\-assisted software engineering tools as part of delivery leadership and engineering execution (e.g. Claude Code, GitHub Copilot, Codex etc.)
- Familiarity with GPU\-accelerated AI workloads and cloud AI services for model inference and deployment at scale (e.g. NVIDIA GPU platforms, Azure ML, AWS SageMaker etc.)
- Familiarity with agile and modern engineering delivery methodologies as applied to AI/ML initiatives
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 $76,200 to $174,100\. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $91,400 to $197,900\. 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 $76K-$197K range is below 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 ($137K) sits 36% below the category median. Disclosed range: $76K to $197K.
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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