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About This Role
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:
About Crowe AI Transformation
Everything we do is about making the future of human work more purposeful. We do this by leveraging state\-of\-the\-art technologies, modern architecture, and industry experts to create AI\-powered solutions that transform the way our clients do business.
The new AI Transformation team will build on Crowe’s established AI foundation, furthering the capabilities of our Applied AI / Machine Learning team. By combining Generative AI, Machine Learning and Software Engineering, this team empowers Crowe clients to transform their business models through AI, irrespective of their current AI adoption stage.
As a member of AI Transformation, you will help distinguish Crowe in the market and drive the firm’s technology and innovation strategy. The future is powered by AI, come build it with us.
About the Team
- We invest in expertise. You’ll have the time, space, and support to go deep in your projects and build lasting technical and strategic mastery. You’ll work with developers, product stakeholders, and project managers as a trusted leader and domain expert.
- We believe in continuous growth. Our team is committed to professional development and knowledge\-sharing.
- We protect balance. Our distributed team culture is grounded in trust and flexibility. We offer unlimited PTO, a flexible remote work policy, and a supportive environment that prioritizes sustainable, long\-term performance.
About the Role
Senior AI Engineer I (Senior Staff) leads the design, development, and optimization of advanced AI and machine learning systems with a high degree of autonomy. This role partners closely with architects, product leaders, and engineering teams to deliver end\-to\-end, cloud\-native AI solutions that are scalable, secure, and production\-ready.
As a senior staff\-level individual contributor, the engineer drives technical decision\-making across data, model, and infrastructure layers, resolves complex engineering challenges, and establishes technical standards that elevate team\-wide engineering practices. The role combines deep hands\-on expertise with mentorship, cross\-team collaboration, and strategic input into roadmap planning. This position plays a key role in advancing enterprise AI and generative AI capabilities while ensuring alignment with security, compliance, data governance, and responsible AI standards.
- Designing and implementing complex AI/ML systems, pipelines, and model\-serving architectures for enterprise workloads.
- Developing reusable frameworks, libraries, and tools to accelerate AI engineering across teams.
- Analyzing large\-scale datasets, model telemetry, and inference performance to drive optimization strategies.
- Architecting distributed training, evaluation, and experimentation workflows to improve model reliability and accuracy.
- Collaborating with senior stakeholders to define technical requirements, solution approaches, and feasibility assessments.
- Providing hands\-on technical leadership through design reviews, code reviews, and mentorship of junior and mid\-level engineers.
- Implementing advanced automated testing, including stress testing, bias detection, non\-regression testing, and quality validation.
- Troubleshooting complex pipeline failures, infrastructure issues, and distributed system bottlenecks.
- Optimizing performance across the full model lifecycle, including data preprocessing, training, and inference.
- Documenting architectural decisions, engineering patterns, and best practices to strengthen organizational knowledge.
- Contributing expert\-level recommendations to AI roadmap planning and future capability development.
- Participating in incident response and operational support for deployed AI systems.
- Researching and evaluating emerging AI, generative AI, and cloud technologies for enterprise applicability.
Qualifications
- 4\+ years of professional experience in AI/ML engineering or software engineering.
- Demonstrated experience leading technical efforts or mentoring engineers.
- Deep proficiency in Python, modern ML frameworks, and cloud\-native engineering practices.
- Strong understanding of distributed systems, data pipelines, and model optimization techniques.
- Proven ability to lead technical designs and perform advanced debugging across complex systems.
- Effective communication skills for cross\-functional technical alignment.
- Willingness to travel occasionally for cross\-functional planning and collaboration.
Preferred Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent experience.
- Master’s degree or equivalent advanced study in a relevant technical discipline.
- Cloud and AI certifications, including Azure (AZ\-900, AI\-900, AI\-102, GH\-900, DP/AB series) or equivalent AWS/GCP certifications.
- Databricks Generative AI Associate certification.
- Advanced hands\-on experience with AWS, Azure, or Google Cloud.
- Strong experience with containerization (Docker), Kubernetes (EKS, AKS, GKE), and infrastructure automation (Terraform).
- Experience with cloud ML platforms such as SageMaker, Vertex AI, or Azure ML.
- Hands\-on experience with GPU and accelerator\-based workloads and distributed training jobs.
- Strong knowledge of observability, monitoring, and performance tuning for AI systems.
- Advanced generative AI expertise, including fine\-tuning transformer models using PyTorch and/or TensorFlow.
- Experience designing and deploying RAG systems using vector databases (e.g., Pinecone, Weaviate, FAISS).
- Experience building GenAI services with frameworks such as LangChain or LlamaIndex.
- Experience integrating and evaluating LLM APIs (e.g., OpenAI, Azure OpenAI, Gemini).
- Familiarity with parameter\-efficient fine\-tuning techniques (PEFT, LoRA, QLoRA).
- Experience designing evaluation frameworks for LLM quality, safety, bias, latency, and scalability.
- Demonstrated ability to work independently and solve complex, ambiguous problems.
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 03/31/2026\.
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 $74,100\.00 \- $147,800\.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 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 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, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, Los Angeles County Fair Chance Ordinance, San Francisco Fair Chance Ordinance, and the California Fair Chance Act.
Please visit our webpage to see notices of the various state and local Ban\-the\-Box laws and Fair Chance Ordinances, where applicable.
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 $74K-$147K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Crowe LLP, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($110K) sits 34% below the category median. Disclosed range: $74K to $147K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Crowe LLP AI Hiring
Crowe LLP has 52 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, AI Software Engineer. Positions span New York, NY, US, Livingston, NJ, US, Springfield, IL, US. Compensation range: $135K - $208K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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