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About This Role
AI Datacenter \& Infrastructure Associate VP
Join our AI \& Engineering team by transforming technology platforms, driving innovation, and helping make a significant impact on our clients' success. You'll collaborate with talented professionals, reimagining and re\-engineering operations and processes that are critical to business. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.
AI \& Engineering leverages innovative engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission\-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology \& data platforms. Our delivery models are tailored to meet each client's unique requirements.
Recruiting for this role ends on 06/26/2026\.
Work you'll do
- Drive the strategy and delivery of AI\-centric datacenter transformations: Lead the end\-to\-end planning, design, and execution of complex infrastructure projects, aligning with clients' business and AI objectives.
- Shape client solutions and build trusted relationships with architects and executives: Serve as a strategic advisor to C\-suite and senior IT leaders, translating business needs into innovative infrastructure solutions that enable advanced AI capabilities.
- Oversee multi\-disciplinary project teams: Direct and mentor diverse teams, ensuring seamless collaboration across infrastructure, AI, cloud, and cybersecurity domains to deliver high\-impact outcomes.
- Build and grow the AI Datacenter practice \& capabilities: Lead the growth, strategy, and talent development of the AI Datacenter practice by shaping service offerings, driving innovation, expanding client relationships, and building high\-performing teams to deliver market\-leading infrastructure solutions.
- Advance thought leadership and market presence: Represent the firm at industry events, contribute to go\-to\-market strategies, and drive the development of reusable assets that showcase our expertise in AI datacenter solutions.
- Ensure high\-quality project delivery and client satisfaction: Take accountability for meeting project milestones, managing budgets, and maintaining strong client relationships through proactive communication and solution\-oriented problem solving.
- Promote continuous innovation: Champion emerging technologies and best practices in AI, edge computing, and hybrid cloud, fostering a culture of learning and excellence within the team.
The Team
Our Hybrid Cloud Infrastructure offering delivers specialized engineering capabilities to design, implement, manage, and operate hybrid cloud environments. We modernize networks and AI infrastructure from the core to the edge and develop new infrastructure and device services to help clients stay ahead with the latest technology advancements.
Qualifications
Required
- 10\+ years of experience modernizing and adapting datacenter environments to align with clients' specific requirements, enabling successful execution of tailored AI strategies
- 10\+ years of experience designing architectures that span on\-prem, colocation, and hyperscaler environments; experience with containerized AI workloads (Kubernetes, Kubeflow), data fabric patterns, and edge inference deployment strategies.
- 10\+ years of experience architecting, implementing, and optimizing enterprise\-scale Hybrid Infrastructure Solutions (i.e., GPU platforms, server hardware, AI Transformation)
- 7\+ years or experience designing, delivering, and supporting hybrid cloud and as\-a\-Service solutions, integrating ease\-of\-use features for end\-users, onboarding via ITSM tools (ServiceNow), and ensuring compliance with enterprise security and governance standards.
- 7\+ years of experience implementing infrastructure as code (Terraform, Ansible), AI Ops for predictive maintenance, DCIM modernization, and automation pipelines for provisioning and monitoring GPU clusters.
- 10\+ years of experience in project management, including planning, organizing, and delivering complex infrastructure projects on time and under budget; skilled at managing cross\-functional teams, setting clear objectives, and mitigating risks to ensure project success.
- 12\+ years of relevant consulting or industry experience and large scale, enterprise class consulting or systems integration experience
- 7\+ years of experience modernizing and adapting datacenter environments to align with clients' specific requirements, enabling successful execution of tailored AI strategies
- Bachelor's Degree or equivalent experience
- Ability to travel 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
- Deep knowledge of AI/ML workload characteristics (training vs. inference), GPU/TPU architectures, high\-performance interconnects (InfiniBand, NVLink), and energy\-efficient cooling/power systems (liquid cooling, DCIM, sustainability metrics)
- Strong financial modeling skills for TCO/ROI, capacity planning for AI workloads, and applying ESG principles (e.g., PUE optimization, carbon\-aware computing) to infrastructure investments.
- Proven ability to lead cross\-functional teams delivering AI\-ready data center transformations; adept at executive storytelling, client relationship development, and managing multi\-million\-dollar transformation portfolios.
- Understanding of hyperscaler services (AWS Bedrock, Azure AI, GCP Vertex AI), colocation and interconnect providers, and hardware ecosystems (NVIDIA DGX, Dell, HPE, Supermicro). Able to architect vendor\-neutral and partnership\-driven client strategies.
- Ability to make and meet commitments on a tight timeline with right\-sized scoping on "must haves" to realize customer wins.
- High executive presence and presentation skills with a high degree of comfort with both large and small audiences, with varying degree of technical depth
- Experience collaborating with cross\-functional teams (Sales, Marketing, Product) to develop and execute market expansion strategies.
- Proven ability to collaborate with Data Scientists and ML Engineers to understand infrastructure needs and deliver robust, scalable solutions.
Wages \+ Salary
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 to $ 338,300 . 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 $171K-$338K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. This role's midpoint ($254K) sits 42% above the category median. Disclosed range: $171K to $338K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Deloitte AI Hiring
Deloitte has 72 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, Data Scientist, AI Software Engineer. Positions span New York, NY, US, Arlington, VA, US, Gilbert, AZ, US. Compensation range: $121K - $372K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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