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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:
Senior AI Engineer 1
About Crowe Studio
Crowe Studio is a business unit dedicated to helping clients scale smarter, transform faster, and lead in a platform\-driven economy. Built for speed and flexibility, Crowe Studio operates outside the constraints of traditional professional services delivery models, giving clients access to cross\-disciplinary innovation, embedded AI capabilities, and global delivery resources—all in service of solving complex business challenges in faster, smarter ways.
Through Crowe Studio, the firm provides clients with an innovation partner focused on rapid execution, deep technology integration, and high\-impact results. We’re building the next generation of business models for professional services, where human expertise and AI are embedded within clients’ operations to drive ongoing impact, not just deliver isolated projects.
As a member of Crowe Studio, 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 Forward Deployed Engineering
The Forward Deployed Engineering (FDE) Practice drives AI transformation through embedded consulting and engineering, enabling clients to accelerate delivery, scale solutions, and capture value faster. Our approach helps companies prove AI in their own environments, delivering results in weeks, not months.
We embed our Forward Deployed Engineers side\-by\-side with client teams to spot opportunities, make AI concepts real, and deploy solutions rapidly. Unlike traditional consulting approaches that rely on documented requirements and lengthy development cycles, FDEs blend engineering, product thinking, workflow design, and business understanding to achieve shared, outcome\-driven ownership until measurable results are achieved.
Our engagements range from 2\-hour AI Immersion Labs that turn AI noise into real use cases, to multi\-week Proof of Value Sprints that prototype and deploy game\-changing AI solutions. We help companies de\-risk AI adoption while demonstrating solid ROI.
About the Team
- We invest in expertise. You’ll have the time, space, and support to go deep on client engagements and build lasting technical and strategic mastery. You’ll work with cross\-functional teams and client teams as a trusted advisor 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
Role Overview: The Senior AI Engineer 1 (Senior Staff) leads the development of advanced AI and machine learning systems with a high degree of autonomy. This role partners closely with architects and product stakeholders to design end\-to\-end AI solutions, optimize model performance, and ensure scalable, cloud\-native deployment. The Senior Staff engineer drives technical decision\-making, performs in\-depth system analysis, and resolves complex engineering challenges across data, model, and infrastructure layers. The role is responsible for producing high\-quality, well\-architected solutions and setting technical standards that elevate engineering practices across the team. Senior AI Engineer 1 mentors junior engineers, supports cross\-team collaboration, and contributes significantly to roadmap planning. This position establishes itself as a strong individual contributor with deep technical expertise in AI engineering and generative AI technologies.
In this role, you will:
- Design and implement complex AI/ML systems, pipelines, and model\-serving architectures for enterprise workloads
- Lead development of reusable frameworks, libraries, and tools to accelerate AI engineering across teams
- Analyze large\-scale datasets, model telemetry, and inference performance to drive optimization strategies
- Architect distributed training and model evaluation workflows that improve reliability and accuracy
- Collaborate with senior stakeholders to define solution approaches, technical requirements, and feasibility assessments
- Guide junior and mid\-level engineers through design reviews, code reviews, and hands\-on technical mentorship
- Implement advanced automated testing, including stress testing, bias detection, non\-regression testing, and quality evaluations
- Troubleshoot complex pipeline failures, infrastructure errors, and distributed system bottlenecks
- Document architectural decisions, engineering patterns, and best practices to elevate organizational knowledge
- Optimize performance across all stages of model lifecycle, including preprocessing, training, and inference
- Participate in roadmap discussions and provide expert\-level technical recommendations for future AI capabilities
- Ensure alignment with security, compliance, data governance, and responsible AI guidelines
- Research new generative AI, machine learning, and cloud technologies to evaluate applicability to enterprise use cases
- Contribute to incident response and operational support for deployed AI systems
Qualifications
- 4–6 years of professional AI/ML engineering or software engineering experience
- Deep proficiency in Python, ML frameworks, and cloud\-native engineering
- Strong understanding of distributed systems, data pipelines, and model optimization; ability to lead technical designs and perform advanced debugging
- Advanced hands\-on experience with AWS, Azure, or Google Cloud; strong containerization expertise (Docker); production deployment using Kubernetes (EKS/AKS/GKE)
- Proficiency with Terraform and infrastructure automation; deep experience with cloud ML platforms (SageMaker, Vertex AI, Azure ML)
- Hands\-on background with GPU/accelerator workflows; building and optimizing distributed training jobs; strong knowledge of observability and monitoring tools
- Expertise with PyTorch and/or TensorFlow; advanced experience fine\-tuning transformer architectures using Hugging Face
- Hands\-on experience designing RAG systems with vector databases including Pinecone, Weaviate, or FAISS; building GenAI microservices using LangChain or LlamaIndex
- Demonstrated success evaluating and integrating LLM APIs (OpenAI, Azure OpenAI, Gemini); hands\-on implementing PEFT and LoRA/QLoRA fine\-tuning techniques
- Skilled in designing LLM evaluation suites covering quality, safety, latency, and bias; track record optimizing inference at scale
- Proficiency with low\-code platforms including Microsoft Power Platform (Power Apps, Power Automate, AI Builder, Copilot Studio); experience developing APIs and SDKs that enable low\-code AI consumption and building agents with multi\-step reasoning and tool orchestration
- Effective communication for cross\-functional technical alignment; demonstrated ability to work independently and handle complex problems
- Demonstrated ability to own a complete workstream lifecycle with minimal supervision
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related technical field; Master’s degree or equivalent advanced study preferred
- Travel for this role may be up to 80%, based on client and project needs. Actual travel requirements may vary
Preferred Qualifications
- History of leading technical workstreams, mentoring engineers, or driving complex AI projects through full delivery lifecycle
- 2\+ years as a senior individual contributor with full\-cycle project delivery and business development contribution
- History of supporting sales pursuits or business development initiatives
- Track record of engaging independently with executive or C\-suite stakeholders
- Established record of leading process improvement or innovation initiatives
- Confident presenter adaptable to any audience or format
Minimum Knowledge Expectations
All candidates for Crowe Studio positions are expected to demonstrate baseline AI and technical knowledge, regardless of role or level.
Core Knowledge Areas
All Crowe Studio team members are expected to have working knowledge in the following areas:
- Basic ML/AI literacy (training vs inference, knowledge cutoffs, LLM fundamentals)
- Prompt engineering and instruction hierarchies
- Context window and context management
- Model selection and capabilities
- Fine\-tuning vs prompting vs RAG
- Hallucination and grounding strategies
- Guardrails and output validation
- Evals and testing approaches
- Security and PII awareness
- Responsible AI and governance
- LLM APIs and integration standards
- RAG and vector search
- Vector databases and embeddings
- Agentic workflows and tool use
- Cost and performance awareness
- Common enterprise use cases
What We Look For
Beyond technical knowledge, we look for candidates who demonstrate:
- Intellectual curiosity – asking thoughtful questions and seeking deeper understanding
- Attention to detail – noticing subtle issues and inconsistencies
- Analytical thinking – breaking down complex problems and thinking critically
- Tenacity – following issues through to resolution, even when challenging
- Strong communication – conveying ideas clearly to technical and non\-technical audiences
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.
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.
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 $74K-$147K range is in the lower quartile 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 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($110K) sits 38% below the category median. Disclosed range: $74K to $147K.
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.
Crowe LLP AI Hiring
Crowe LLP has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $147K - $204K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 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 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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