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Your Journey at Crowe Starts Here:
At Crowe, you can build a meaningful and rewarding career. With real flexibility to balance work with life moments, you’re trusted to deliver results and make an impact. We embrace you for who you are, care for your well\-being, and nurture your career. Everyone has equitable access to opportunities for career growth and leadership. Over our 80\-year history, delivering excellent service through innovation has been a core part of our DNA across our audit, tax, and consulting groups. That’s why we continuously invest in innovative ideas, such as AI\-enabled insights and technology\-powered solutions, to enhance our services. Join us at Crowe and embark on a career where you can help shape the future of our industry.
Job Description:
What It Means to Be a Consultant at Crowe
Consulting is a dynamic business focused on solving problems for our clients and serving our core markets through innovative solutions. As technology and AI continue to reshape the consulting landscape, we are looking for individuals who are curious, adaptable, and eager to learn. At Crowe, consultants are expected to build both technical and transferable skills, think critically, and use technology to solve real business problems. In this role, you will continuously learn, collaborate across teams, and explore how tools, including emerging AI capabilities, can improve efficiency, insights, and client outcomes.
In management at Crowe, you play a pivotal role in leading teams, guiding project execution, and deepening client relationships. You are expected to contribute to account planning, identify opportunities to add value, and ensure high\-quality delivery. As your responsibilities expand, you take on broader account ownership, balancing project leadership with growing involvement in client strategy and solution development.
Success in this role comes from a growth mindset, strong communication skills, advanced critical thinking, and the ability to navigate new challenges with confidence.
AI Governance Consulting – Manager
At Crowe, you can build a meaningful and rewarding career. With real flexibility to balance work with life moments, you’re trusted to deliver results and make an impact. We embrace you for who you are, care for your well\-being, and nurture your career. Everyone has equitable access to opportunities for growth and leadership. Over our 80\-year history, delivering excellent service through innovation has been core to our DNA across our audit, tax, and consulting teams.
Crowe’s AI Governance Consulting team helps organizations build, assess, run, and audit responsible AI programs. We align AI practices with business goals, risk appetite, and evolving regulations and standards (e.g., NIST AI RMF 1\.0, ISO/IEC 42001, EU AI Act), enabling clients to adopt AI confidently and safely.
As an AI Governance Manager, you’ll lead client delivery, drive sales enablement, and shape our AI Governance offering. You will manage projects end\-to\-end, mentor teams, and collaborate across Crowe (Cyber, Risk, Legal/Privacy, Security, Enterprise Risk Management, Model Risk Management, and Audit) to bring integrated solutions to market.
You’ll be responsible for:
- Client delivery \& program build: Lead engagements to stand up or mature AI governance programs across the lifecycle (strategy, policy, controls, operating model, metrics). Map practices to frameworks such as NIST AI RMF and ISO/IEC 42001; translate requirements into practical controls, workflows, and guardrails.
- Assessment \& assurance: Plan and execute current\-state assessments, model/system reviews, control testing, and readiness audits for AI uses (including genAI) against policy and regulatory expectations (e.g., EU AI Act obligations/timelines). Deliver clear findings and prioritized roadmaps.
- Run\-state operations: Design operating rhythms (intake, review/approval, model registry, risk scoring, human\-in\-the\-loop, monitoring, incident management) and help clients operationalize “three lines” responsibilities with measurable KPIs.
- Sales enablement: Partner with teams to qualify opportunities, shape solutions/SOW/ELs, develop proposals and pricing, and contribute to pipeline reviews. Build client\-ready collateral.
- Offering development: Evolve Crowe’s AI Governance methodologies, accelerators, control libraries, templates, and training. Incorporate updates from standards/regulators into our playbooks (e.g., NIST’s GAI profile).
- Thought leadership: Publish insights, speak on webinars/events, and support marketing campaigns to grow brand presence.
- People leadership: Supervise, coach, and develop consultants; manage engagement economics (scope, timeline, budget, quality) and support recruiting.
Qualifications:
Required
- 3\+ years hands\-on AI governance/Responsible AI experience (policy, controls, risk, compliance, or assurance of AI/ML systems).
- 5\+ years in compliance, risk management, and/or related advisory roles with client\-facing delivery and team leadership.
- Bachelor’s degree required; advanced degree in a relevant field (e.g., information systems, public policy, statistics, law) a plus.
- Financial Services industry experience, with demonstrated knowledge of the regulatory, risk management, compliance, and governance environment applicable to financial institutions.
- Demonstrated ability to translate regulatory/standard requirements (e.g., NIST AI RMF, EU AI Act) into actionable policies, processes, and controls.
- Prior experience should include progressive responsibilities, including supervising and reviewing the work of others, and project management, including self\-management of simultaneous work\-streams and responsibilities.
- Strong written and verbal communication and comprehension both formally and informally to our clients and our teams, in a variety of formats and settings, including in interviews, meetings, calls, e\-mails, reports, process narratives, presentations, etc.
- Networking and relationship management.
- Willingness to travel.
Preferred
- Professional services/consulting experience strongly preferred, particularly experience serving Financial Services clients in risk, regulatory, technology, data, model risk, or AI governance\-related engagements.
- Certification: AIGP – Artificial Intelligence Governance Professional (IAPP) or equivalent credential in AI governance/privacy/risk (e.g., CIPP/CIPM/CIPT with AI coursework, ISO/IEC 42001 implementer/auditor).
- Experience with genAI risk controls (prompt/data controls, evaluation, monitoring) and model documentation/testing practices.
- Familiarity with GRC, data, and model tooling (e.g., ServiceNow/Archer, Collibra/Alation, model registries/ML platforms).
- Experience coordinating across privacy, security, risk, and engineering functions; understanding of U.S. federal/state AI policy landscape (e.g., EO 14110 and subsequent developments).
\#AIgovernance \#ArtificialIntelligence \#Governance \#FinancialServices \#RiskConsulting \#GoCrowe
We expect the candidate to uphold Crowe’s values of Care, Trust, Courage, and Stewardship. These values define who we are. We expect all of our people to act ethically and with integrity at all times.
The application deadline for this role is 08/21/2026\.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire. Crowe is not sponsoring for work authorization at this time.
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 Crowe, 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 $95,500\.00 \- $195,400\.00 per year. Our Benefits:
Your exceptional people experience starts here. At Crowe, we know that great people are what makes a great firm. We care about our people and offer employees a comprehensive total rewards package. Learn more about what working at Crowe can mean for you!
How You Can Grow:
We will nurture your talent in an inclusive culture that values diversity. You will have the chance to meet on a consistent basis with your Career Coach that will guide you in your career goals and aspirations. Learn more about where talent can prosper!
More about Crowe:
Crowe (www.crowe.com) is one of the largest public accounting, consulting and technology firms in the United States. Crowe uses its deep industry expertise to provide audit services to public and private entities while also helping clients reach their goals with tax, advisory, risk and performance services. Crowe is recognized by many organizations as one of the country's best places to work. Crowe serves clients worldwide as an independent member of Crowe Global, one of the largest global accounting networks in the world. The network consists of more than 200 independent accounting and advisory services firms in more than 130 countries around the world.
Crowe LLP and Crowe Advisory LLC (and their respective subsidiary entities) provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, sexual orientation, gender identity or expression, genetics, national origin, disability or protected veteran status, or any other characteristic protected by federal, state or local laws.
Crowe LLP and Crowe Advisory LLC (and their respective subsidiary entities) does not accept unsolicited candidates, referrals or resumes from any staffing agency, recruiting service, sourcing entity or any other third\-party paid service at any time. Any referrals, resumes or candidates submitted to Crowe, or any employee or owner of Crowe without a pre\-existing agreement signed by both parties covering the submission will be considered the property of Crowe, and free of charge.
Crowe will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws.
Please visit our webpage to see notices of the various state and local Ban\-the\-Box laws and Fair Chance Ordinances, where applicable.
We are committed to a merit\-based hiring process, evaluating all candidates consistently using objective, job\-related criteria such as relevant experience, demonstrated skills, measurable impact, and alignment with the role’s responsibilities, and making employment decisions in a fair and inclusive manner free from discrimination.
If you are interested in applying for employment with Crowe and are in need of an accommodation or require special assistance to navigate our website or to complete your application, please visit our Applicant Assistance and Accommodations page for more information: https://careers.crowe.com/crowe\-applicant\-assistance\-and\-accommodation
Salary Context
This $95K-$195K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Crowe, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $95K to $195K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Crowe AI Hiring
Crowe has 10 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Livingston, NJ, US, Chicago, IL, US, New York, NY, US. Compensation range: $87K - $213K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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