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About This Role
If you are a technology visionary with a passion for transforming global tax business with digital technology, consider working with the US Tax Transformation technology team. This is an exciting opportunity to support global execution of Deloitte's tax strategy as we shift from "doing digital" to "being digital" by reimagining how we engage with our clients, deliver our services, operate our business, and create value.
Recruiting for this role ends on May 31, 2027
Work you'll do
As a Deloitte Senior, Business Analyst, you will be responsible for understanding the business needs, models, trends, and transformation vision. You will identify changes required to meet the business need by consideration of organization structure, business functions, required knowledge and skills, processes and technology. You will explore possible solutions by coordinating with technical architects for discovery and demonstrate how proposed solutions are aligned to business goals and objectives and define the impacts and risks.
Responsibilities:
- Use business goals and objectives to assist Product Owners in developing a product roadmap.
- Perform business analysis tasks and/or coordinate tasks across the Service Delivery Transformation program.
- Identify solution options that meet the business need, then coordinate with delivery teams to determine the best solution approach.
- Assist Product Owners in defining release scope, including defining benefits and risks during the discovery phase.
- Develop supporting artifacts including requirements, solution options, recommendations, business flows, where required.
- Make connections with other initiatives in place, underway or planned across other parts of the organization that need to be considered.
- Work with architects and delivery teams to ensure the functionality meets the needs of the enterprise and aligns to the overall vision.
- Ensure the business is aware of emerging trends, practice, and technologies and how they may contribute to business outcomes and strategies.
- Actively identify and champion innovation initiatives supporting the business strategy.
- Develop and maintain productive and collaborative relationships with Product Owners, and various SMEs across the organization to support Service Delivery Transformation efforts.
A successful candidate would possess these skills:
- Strong business analysis and problem-solving skills, with the ability to translate business needs into practical solution options.
- Excellent communication and facilitation skills, with confidence engaging Product Owners, architects, SMEs, and delivery teams.
- Strong collaboration and relationship-building skills, able to work effectively across functions and influence without formal authority.
- Agile and adaptable mindset, with the ability to manage shifting priorities, support transformation efforts, and contribute to innovation.
The Team
Deloitte Tax LLP's Tax Transformation Office (TTO) is responsible for the design, development, and deployment of innovative, enterprise technology, tools, and standard processes to support the delivery of tax services. The TTO team focuses on enhancing Deloitte Tax LLP's ability to deliver comprehensive, value-added, and efficient tax services to our clients. It is a dynamic team with professionals of varying backgrounds from tax technical, technology development, change management, Six Sigma, and project management. The team consults and executes on a wide range of initiatives involving process and tool development and implementation including training development, engagement management, tool design, and implementation.
Qualifications
Required:
- Ability to perform job responsibilities within a hybrid work model that requires US Tax professionals to co-locate in person 2 - 3 days per week
- Bachelor's degree in Business Administration Information Systems, Computer Science or other relevant discipline
- 3+ years' supporting business analysis, technology transformation, or Agile delivery initiatives.
- Demonstrated experience working in Agile/Scrum delivery environments, including facilitating requirements elicitation and decomposition into user stories, features, and tasks.
- Successful experience using Azure DevOps to manage work items and understand dependency link types such as Parent/Child, Predecessor/Successor, and related relationships.
- Limited immigration sponsorship may be available.
- Ability to travel up to 10%, on average, based on the work you do and the clients and industries/sectors you serve\One of the following active accreditations obtained:
- + Licensed CPA in state of practice/primary office if eligible to sit for the CPA + If not CPA eligible: + - Licensed Attorney
- Enrolled Agent
- CBAP® - Certified Business Analysis Professional
- Certified SAFe® Agilist
- Certified SAFe® Product Owner / Product Manager
- Microsoft Azure
- Project Management Professional (PMP)
Preferred:
- Previous experience in a tax, financial services, or professional services environment
- Strong verbal and written communication skills, with the ability to tailor messaging for both technical and non-technical audiences
- Strong listening, facilitation, and stakeholder management skills, with experience driving alignment across diverse groups
- Broad awareness of technology tools and platforms, with the ability to evaluate their practical application to business needs
- Experience supporting business process improvement, transformation, or technology-enabled change initiatives
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $95,550 to $195,250.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
Salary Context
This $95K-$195K range is below 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 Deloitte, 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 ($145K) sits 31% below the category median. Disclosed range: $95K to $195K.
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.
Deloitte AI Hiring
Deloitte has 112 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist, AI Agent Developer. Positions span New York, NY, Perth, Western Australia, Australia, Princeton, NJ, US. Compensation range: $195K - $307K.
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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