Interested in this AI/ML Engineer role at Deloitte?
Apply Now →About This Role
Role Overview:An expert User Experience (UX) specialist with multi\-facet role to drive customer centric mindset for creating products \& solutions for various business units and develop capabilities and skills of the Experience team.
As an Experience Design specialist for Deloitte's DT\-US Product Engineering team, you will be tasked with solving complex challenges through elegant, user\-centered design solutions informed by a deep understanding of our users. You are a lean UX expert and will lead the creation of design strategy and vision, shaping big\-picture workflow and product direction while also delivering high\-quality visual and interaction design elements.
A highly experienced and versatile UX generalist with deep expertise in interaction design, you will feel equally at home leading design teams as you are designing user experiences from concept to launch. You have a deep understanding of a product\-led approach and working in small, empowered product teams to design \& deliver impactful experiences. You will apply behavioral metrics, user research findings, and other data\-driven insights to design innovative product solutions that meet our users' needs with exceptional experiences. Join us in shaping the future of design!
Work you'll do:
- Provide creative and strategic leadership for design, collaborating with empowered product teams to design UX solutions that align with business objectives.
- Spearhead the creation of Experience\-led vision, and create design assets to bring this vision to life for the organization
- Lead cross\-functional workshops and exercises for Product teams and stakeholders to understand users, clarify the problem to solve, and brainstorm innovative solutions anchored in human\-centered design.
- Leads teams in framing and solving hard experience problems; Drives innovative UX efforts that uncover new user value with new kinds of experiences.
- Advocate for user\-centered design best practices within product development. An expert and evangelist for all things user\-centered design.
- Partner with Product and Engineering leaders across the organization
- Connects the entire Product Engineering team with users through presentations of user research results and behavioral analytics
- Provide guidance and direction for key UX core initiatives such as Design System development
- Create strategic design deliverables such as strategy decks, customer journeys, visions of future experiences and evangelize these cross\-product "blueprints" across teams
- Actively engage in hands\-on Experience craft modeling by deep participation in projects
- Work with design, research, program management, and product leaders on process for product development
- Play lead UX role across multiple products, guiding and mentoring multi\-disciplinary UX team members.
- Collaborate with cross\-functional empowered product teams, applying lean UX methods, and define success metrics and ensure alignment between UX outcomes and business goals. Connect design with business value for impact.
- Drive insight studies related to the discovery and understanding of unresolved interface problems and needs and lead design thinking workshops to create innovative, impactful, and valuable solutions
- Measure and drive user experience KPIs, ensuring high standards of user\-centered design and rapid iteration for stakeholder and user satisfaction.
- Act as a thought leader, driving innovation and removing roadblocks for experience teams to deliver groundbreaking solutions.
- Stay current with trends, best practices, and methodologies in areas of UX, information architecture, UI design, behavioral analytics, and user research.
- Drive programs that enhance user experience and brand consistency using modern intelligence (e.g., GenAI) to achieve business goals.
- Develop talent strategies, recruit, coach, and mentor multi\-disciplinary Experience team, aligning with lean UX principles and business goals.
The team:
US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost\-effective model that focuses on value/outcomes that leverages a progressive and responsive talent structure. As Deloitte's primary internal development team, Product Engineering delivers innovative digital solutions to businesses, service lines, and internal operations with proven bottom\-line results and outcomes. It helps power Deloitte's success. It is the engine that drives Deloitte, serving many of the world's largest, most respected companies. We develop and deploy cutting\-edge internal and go\-to\-market solutions that help Deloitte operate effectively and lead in the market. Our reputation is built on a tradition of delivering with excellence.
Qualifications:
Required:
- Expert User Experience Leader and Practitioner with 8\-15 years of leading holistic customer / user experiences for multiple products, demonstrating measurable customer adoption and success metrics.
- Master of User Experience craft, fostering a culture of learning and innovation, and recognized as an expert in modern UX Design and Research
- Experienced mentor with strong leadership skills, inspiring self\-development and continuous learning in junior practitioners.
- Expert analytical and problem\-solving skills, with a detail\-oriented, organized, experimental, and visionary approach. Strategic systems thinker.
- Expert in all things user\-centered design (UCD), including user research, A/B testing, rapid prototyping, heuristic analysis, addressing usability, accessibility, etc.
- Expert in creating reusable design frameworks (e.g., Design Systems) to streamline design\-to\-implementation processes
- Humble, curious, and value\-oriented, understanding the iterative nature of product development and favoring action and learning over exhaustive upfront planning.
- Inspirational player\-coach, capable of leading teams through transformation and playing an active role as a product experience leader.
- Skilled in using rapid experimentation to develop lean and simple solutions that address customer needs efficiently and effectively.
- Passionate about all things UX and other areas of design and innovation
- Expert with lean UX tools such as Figma, Adobe CC, Sketch, Axure, InVision, and more.
- Exceptional communication, collaboration, and leadership skills, with the ability to influence at all levels.
- Bachelor's degree in design, psychology, cognitive science or related field; Advanced degree is a plus.
- Experience in DesignOps is a plus.
- A strong portfolio or samples of work demonstrating experience in UX skills is required.
- Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve.
- Limited immigration sponsorship may be available
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 $137,000 to $282,000\.
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 $137K-$282K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 in Demand for This Role
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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($209K) sits 13% above the category median. Disclosed range: $137K to $282K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Deloitte AI Hiring
Deloitte has 69 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Consultant, Data Scientist. Positions span Baltimore, MD, US, Jersey City, NJ, US, Stamford, CT, US. Compensation range: $140K - $372K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.