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About This Role
The Innovation & Delivery Transformation (I&DT) team is building the future of Deloitte's business through new AI native platforms and products The team is responsible for identifying, nurturing, scaling, and winning in new markets through new capabilities. Rather than relying on what the firm has historically done, I&DT looks ahead and invests in areas where growth is expected three, five, and ten years into the future.
A core focus of this role is Zora AI by Deloitte, Deloitte's Agentic Platform for the modern business. Zora AI by Deloitte enables enterprise organizations across all industries to make faster, more precise, and more customer-obsessed decisions aided by AI. The platform combines enterprise grade concepts with leading edge AI capabilities in a subscription-based AI platform to support repeatable, scalable decisions across Finance, Procurement, Human Resources, Information Technology and other enterprise activities.
Recruiting for this role ends on 11/30/2026.
Work you'll do
Deloitte Consulting is seeking a high-performing VP Sales Executive to drive new client adoption and growth of product revenue for Zora AI by Deloitte as a product suite. This leader will be responsible for originating, shaping, and closing product-led engagements across the portfolio.
This role is also critical for feedback to shape and improve the products in the portfolio. The VP Sales Executive role requires a commercially driven executive with a strong background in enterprise data sales, competitive data environments, consultative solution selling, and executive-level relationship development.
Sales Leadership
- Shape and sell annual recurring revenue deals across the Zora AI product suite.
- Serve as a leader for feedback to shape product development, product marketing, and pricing the Zora AI portfolio.
- Develop and execute account-based sales strategies aligned to priority Zora AI by Deloitte accounts.
- Expand sales opportunities for Zora AI products.
- Lead end-to-end sales cycles: origination, qualification, shaping, pricing, negotiation, and close.
Market Expansion
- Identify whitespace opportunities within existing Deloitte accounts.
- Open new logo opportunities.
- Position Zora AI as a strategic transformation platform, not just a point solution.
Executive Engagement
- Build C-suite and SVP-level relationships.
- Collaborate with Partners, Market Makers, Product Leads, and Pursuit Teams to align offerings with priority client opportunities and growth agendas.
- Articulate ROI, value realization, and measurable business outcomes.
Cross-Functional Collaboration
- Partner with Zora AI by Deloitte leaders, industry account teams, and product teams.
- Provide structured pipeline visibility and forecast accuracy.
- Find, secure and direct talent internally or externally to produce compelling content like product demos, PowerPoint overviews, and other sales collateral.
- Coach and support team members on effective client engagement and market maker interactions.
- Increase Offering Portfolio, Industry, and field awareness of Zora AI by Deloitte product capabilities and differentiation through targeted enablement efforts.
- Capture field insights, client feedback, and market learnings and feed them into ongoing product improvement cycles.
The successful candidate will possess:
- Demonstrated ability to operate effectively across both business and technical audiences, including senior executives, Partners, product leaders, and engineers.
- Proven communication and storytelling skills, including experience presenting to executive audiences and structuring complex ideas into clear, actionable narratives.
- Strong organizational and prioritization skills, with experience managing multiple workstreams simultaneously.
- Demonstrated interest in developing deep expertise in Zora AI by Deloitte products or prior experience working with Zora AI or similar Enterprise grade platforms.
- Demonstrated willingness and ability to contribute to both strategic planning and hands-on execution.
- Demonstrated track record of client leadership, selling, and deal shaping
The Team
The I&DT team collaborates closely with Industries, Offering Portfolios, Growth, and Delivery Transformation to curate and scale the Zora AI by Deloitte portfolio, drive Strategic Growth Offerings, and translate differentiated product capabilities into compelling client outcomes. The team also plays a central role in incubating next-generation technologies through Deloitte's Technology Offices and leading the firm's Tech Talent Transformation. Innovation & Delivery Transformation is part of Deloitte Consulting Services and brings an engineering-, product-, and platform-oriented mindset to everything it does. The team helps Deloitte organize for rapid innovation, expand how technology is built and commercialized, and support clients as they navigate disruption in an increasingly data- and AI-driven consumer economy.
Qualifications
Required:
- 10+ years of professional experience in data or product selling, solution development, product commercialization, or related roles within enterprise software, analytics platforms, data provider, or management consulting.
- Ability to travel 0-50%, on average, based on the work you do and the clients and industries/sectors you serve.
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
Preferred:
- Bachelor's degree
- Demonstrated commercial experience with technology products or platforms, including product marketing, go-to-market strategy, sales enablement, pricing and packaging, or commercialization of analytics, data, or AI-driven products.
- Familiarity with enterprise systems and platforms such as SAP, Oracle, Salesforce, and supply chain, merchandising, or marketing technology platforms
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 $171,600.00 to $338,300.00.
You may also be eligible to participate in a sales incentive program, whereby you may be eligible to receive certain incentive compensation amounts based on achievement of certain sales goals set forth each year, subject to the terms and conditions of any applicable program document.
Salary Context
This $171K-$338K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. This role's midpoint ($254K) sits 20% above the category median. Disclosed range: $171K to $338K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Deloitte AI Hiring
Deloitte has 104 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, AI Software Engineer. Positions span Jersey City, NJ, Rosslyn, VA, US, Morristown, NJ, US. Compensation range: $121K - $338K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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