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About This Role
Do you have a passion for artificial intelligence, machine learning, and data analysis? Do you yearn to have the impact of your work recognized and valued by more than just your development team?
If yes, we have just the role for you.
In Deloitte's Audit and Assurance business, we make businesses and markets better. An audit is more than an obligation; it is an opportunity to see further and deeper into businesses. In our role as independent auditors, we enhance trust in the companies we audit, helping a multitrillion dollar capital markets system function with greater confidence. As we aspire to the very highest standards of audit quality, we deliver deeper insights that can help clients become more effective organizations.
You will be joining our collaborative Data Science team which leverages the most advanced technologies in machine learning and artificial intelligence to achieve the Deloitte Audit and Assurance vision of an AI-enabled audit. Your work will span from research to deployment of production-ready AI solutions, and continued development. Some of our current projects focus on generative AI, prompt engineering, large language models (LLMs), agentic systems, anomaly detection, clustering, and knowledge graphs.
Work you'll do
As a Lead AI and Data Science Engineer II, you will lead the technical aspects of our Data Science projects and the development of AI and machine learning (ML) solutions. You will collaborate closely with audit subject matter experts, product teams, and software developers to develop use case requirements and bring solutions to production. You will manage Junior and Senior Data Scientists to complete the Data Science objectives that are required to fully develop and deploy production-level ML solutions. This includes performing or guiding research in an independent fashion and interacting closely with key stakeholders with varying levels of machine learning experience.
Specifically, you will be expected to:
- Lead the application of rigorous data science within your workstream, managing and supervising junior resources through the entire data science development and deployment lifecycle
- Collaborate with subject matter experts to obtain a deep understanding of the underlying business problem, and to define and refine the corresponding technical solution
- Design and manage project plans, metrics, and objectives and key results that align with the group's mission of an AI-enabled audit
- Identify and communicate risks that emerge in the development lifecycle
- Stay up to date with the latest trends, techniques, and advancements in data science and identify opportunities for their application
- Actively mentor Junior and Senior Data Scientists on professional development including regular feedback and staff touchpoints
- Architect ML pipelines and actively contribute high-quality, production-ready code (readable, well-tested, with well-designed APIs)
A successful candidate would possess these skills:
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to mentor and provide clear guidance to others
The team
Our team culture is collaborative and encourages team members to take initiative and seek on-the-job learning opportunities. Audit & Assurance services are focused on engagements related to independent External Audit services, Accounting, Controls & Reporting Advisory, and Specialized Assurance & Sustainability. We bring together the diverse skills and industry experience of our people, leading-edge technology, and a global network to deliver high-quality audits of financial statements and internal controls over financial reporting, along with assurance reports and valuable advice and insights across the corporate reporting landscape. Learn more about Deloitte Audit.
Qualifications
Required:
- Bachelor's degree in related field
- 6+ years of relevant industry experience with design, development, and deployment of machine learning models
- Experience being the technical lead for multiple project teams simultaneously
- Expert understanding of the state of the art of two or more fields in artificial intelligence; NLP and generative AI, probabilistic graphical models, time-series analysis, weak supervised learning, etc.
- Understanding of LLMs, prompt engineering, and use of evaluations in development and validation
- Previous experience mentoring, training, and developing junior members of the team; experience in employee performance reviews
- Expert understanding of Python and other common languages
- Demonstrated ability to lead the design and implementation of machine learning solutions with a focus on maintainability and scalability through the entire development life cycle
- Extensive experience using common machine learning and deep learning frameworks such as TensorFlow, PyTorch, OpenAI, and LangChain
- Extensive experience with at least one cloud-based ecosystem (Azure, GCP, AWS)
- Experience in an Agile working environment and at least one related project management tool (Azure DevOps, Jira, etc.)
- Demonstrated ability to write high-quality, production-ready code (readable, well-tested, well-documented, with well-designed APIs)
- Demonstrated ability to develop novel machine learning methods that go beyond putting together existing open-source code, and to apply problem-solving skills to complex issues
- Solid understanding of Docker, Jenkins, Kubernetes, and other DevOps tools
- You should reside within a commutable distance of your assigned office with the ability to commute daily, if required
- You should expect to co-locate with team members or other colleagues in a Deloitte office, at a client site and/or virtually, based on specific team and business expectations
- Ability to travel up to 25%, on average, based on the type of work you perform and the clients served
- Limited immigration sponsorship may be available
Preferred:
- 10+ years of industry experience leading the design, development, and deployment of machine learning models
- PhD in a quantitative field (computer science, engineering, mathematics, physics, machine learning, statistics)
- Prior scientific publication history. Outstanding academic track record as evidenced by top tier publications.
- Strong project management and delivery experience, including budget oversight and staffing of project teams including time management
- Extensive experience with Microsoft Azure, including certification in machine learning
- Experience with machine learning pipelines (Azure ML/Databricks)
- Experience with ML Ops and related governance processes, particularly within a regulated industry
- Strong presentation skills using Microsoft Office suite (Visio, PowerPoint, etc.)
- Understanding of the capital markets, and the role public accounting firms play
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 $118,700 to $243,700.
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 $118K-$243K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($181K) sits 15% below the category median. Disclosed range: $118K to $243K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Deloitte AI Hiring
Deloitte has 128 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Safety, AI Product Manager. Positions span Jacksonville, FL, Morristown, NJ, US, New York, NY. Compensation range: $118K - $338K.
Location Context
AI roles in Boston pay a median of $215,000 across 386 tracked positions.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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