Interested in this AI/ML Engineer role at Deloitte?
Apply Now →Skills & Technologies
About This Role
L65 \- Firm Enterprise Solutions Associate Director, Strategy, Growth, and Transformation
Zora AI is Deloitte's AI agent platform delivering role\-/function\-specific agents that integrate with enterprise systems and workflows. As the Lead, Forward Deployed Engineering (FDE), you will define and run the global FDE organization\-setting the charter, operating model, standards, and capacity needed to deliver successful deployments at scale. You will lead multiple FDE teams across US, EMEA, and APAC, oversee critical client engagements, and partner with Product, Engineering, Cyber, and Risk to turn field learnings into repeatable delivery patterns and durable platform improvements.
Estimated Date of Closure June 4, 2026
Work you'll do
- Define the FDE charter and operating model: Establish mission, engagement model, intake/prioritization, team structure, and ways of working across regions and time zones.
- Lead multiple FDE teams globally: Recruit, coach, and performance\-manage FDE managers/leads and individual contributors across US, EMEA, and APAC; build coverage models and on\-call/escalation paths.
- Own delivery excellence and repeatability: Create standardized implementation playbooks, reference architectures, quality gates, and reusable assets to reduce bespoke work and improve time\-to\-value.
- Be accountable for successful deployments: Oversee multiple concurrent client implementations, ensuring scope clarity, environment readiness, risk controls, and predictable outcomes.
- Participate in key client engagements: Serve as executive technical lead on priority accounts\-leading workshops, shaping solution approach, handling escalations, and building trusted client relationships.
- Translate field signals into product improvement: Create closed\-loop mechanisms to convert recurring deployment friction into structured requirements; influence roadmap, connector strategy, observability, and governance features.
- Establish best practices for agent deployments: Standardize patterns for human\-in\-the\-loop approvals, exception handling, evaluation/monitoring, security/privacy, and auditability in enterprise contexts.
- Partner across Deloitte and alliances: Coordinate with Sales, Delivery, Alliances, and Global teams to support pursuits, packaging, and scalable rollout across industries and geographies.
- Run governance and metrics: Track delivery KPIs (time\-to\-value, success rates, incident trends), manage capacity planning, and drive continuous improvement via retrospectives and post\-implementation reviews.
- Risk, security, and compliance leadership: Ensure implementations align to Deloitte/client security requirements, data handling standards, and AI governance expectations; lead resolution of high\-severity risks.
The 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
Qualifications
Required:
- 10\+ years in software engineering, solutions/forward deployed engineering, platform delivery, or technical program leadership in enterprise environments; including leadership of multi\-team organizations.
- Prior experience leading complex customer deployments involving cloud infrastructure, identity/SSO, data access, and integration with enterprise systems.
- Prior experience with GenAI/LLM application delivery (agent workflows, tool orchestration, retrieval\-augmented generation), including operational risks and controls.
- Prior experience establishing standards and operating rhythm (playbooks, quality gates, escalation models, delivery KPIs) across distributed teams.
- Prior experience with executive\-level stakeholder management and able to communicate with client IT/security leaders and business owners; adept at navigating ambiguity and driving decisions.
- Prior experience working across US and global clients, including delivery coordination across time zones and regional constraints (data residency, security, procurement).
- Ability to travel 0\-10%, on average, based on the work you do and the clients and industries/sectors you serve.
- Limited immigration sponsorship may be available.
Preferred:
- Prior experience building or scaling an FDE/solutions engineering organization globally.
- Prior experience and familiarity with enterprise ecosystems such as SAP, Oracle, ServiceNow, Salesforce, and common integration approaches.
- Prior experience and background in regulated industries and governance\-heavy environments (auditability, privacy, retention, model risk).
- Prior experience partnering with product teams on platformization (turning bespoke work into reusable product capabilities).
- Prior enterprise delivery leadership experience driving multi\-workstream execution, program governance, and executive steering across complex stakeholder environments.
Wage Disclosure
- For individuals assigned and/or hired to work in New York, Deloitte is required by law to include a reasonable estimate of the compensation range for this role. This compensation range is specific to {insert location} and 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. 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$186,500 to $311,000\.
- 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.
FOR ALL INTERNAL POSTINGS:
This position is aligned with the Core Model. To view the associated benefit package, please reference this document USBenefitsJourneyCDandETAM
Deloitte is committed to providing reasonable accommodations for people with disabilities. If you require a reasonable accommodation to participate in the recruiting process, please direct your inquiries to the Global Call Center (GCC) at [email protected] .
\#EA\_ExpHire
Salary Context
This $186K-$311K range is above the 75th percentile 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 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 $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 ($248K) sits 34% above the category median. Disclosed range: $186K to $311K.
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
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national 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.