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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
Everything we do is about making the future of human work more purposeful. We do this by leveraging state-of-the-art machine learning, generative AI, modern architectures, and industry experts to create AI-powered solutions that transform the way we do business.
Our world-class AI and ML professionals give Crowe the ability to capture and harness the power of data. A combination of AI and Machine Learning Engineers and AI Platform and Infrastructure Engineers create and deploy AI functionality to solve complex problems. We focus on building scalable AI solutions, based on rigorous data exploration, validation via proof-of-concept buildout, and close partnership with subject matter experts across business areas.
The future is powered by AI, come build it with us.
Role Overview
Crowe’s Studio team is seeking an AI Tech Lead to serve as a hands-on technical leader for the design, development, and lifecycle management of AI agents that accelerate the delivery of Crowe’s Audit services. This role is built around Crowe’s forward deployed engineer (FDE) model: rather than sitting above a project as a traditional program manager, this person sits with Audit practitioners, learns the workflow, designs the agent as it is built, and owns the outcome end to end. The goal is to establish the technical patterns, Skills-based architecture, and engineering practices needed to support a growing portfolio of agents. As that portfolio grows, the role is expected to grow into leading a small team as an engineering manager with full-stack engineers who each own a slice of the roadmap.
Key Responsibilities
Delivery of Audit AI Agents
- Own the design, development, maintenance, and continuous improvement of AI agents and agent-assisted workflows that accelerate the execution of common tasks across Crowe’s Audit practice.
- Operate as a forward deployed engineer paired directly with an Audit SME — sitting with practitioners, learning the workflow firsthand, and designing the agent as it is built rather than from a static spec.
- Remain hands-on in development: write, review, test, and maintain substantial Python codebases and the Skills, instructions, prompts, and supporting resources that define agent behavior.
- Manage project scope, timelines, and stakeholder communication for the agents you own — balancing hands-on delivery with the planning, prioritization, and coordination expected of a senior technical lead.
- Design and develop agents using a Skills-based architecture — modular, reusable capabilities that can be efficiently discovered, invoked, tested, maintained, and enhanced over time.
- Serve as the technical authority for the agent layer, making key design and implementation decisions while partnering with architects and engineering teams to ensure alignment with the broader Audit technology ecosystem.
- Implement and maintain monitoring, observability, and alerting for agents running in production, including trace-level visibility into agent reasoning and tool calls, to proactively detect, diagnose, and resolve issues.
Build and Scale the Engineering Capability
- Establish reusable engineering patterns and development practices that let a growing portfolio of agents be developed, evaluated, deployed, supported, and enhanced sustainably.
- Build and maintain CI/CD pipelines in Azure DevOps to automate testing, evaluation, and deployment of agents across environments.
- As the portfolio grows, lead a small team of forward deployed engineers that advance the roadmap per the audit stakeholders they serve.
- Participate in the development and evolution of agent evaluation strategies, including representative test cases, automated evaluators, regression testing, quantifiable accuracy metrics, and processes for identifying and addressing changes in agent behavior.
- Mentor engineers and provide technical leadership, including code and design reviews, as the agent portfolio and supporting team grow.
- Promote collaboration across the Audit BU and the broader Studio to surface opportunities, share reusable patterns, and avoid duplicated build effort.
Product Judgment
- Think in terms of ROI, business case, and value pool for every initiative, defining and tracking KPIs to measure and communicate business impact; make product decisions directly alongside the SME rather than routing through a separate product owner.
- Demonstrate a solutions-oriented approach: be proactive, identify solutions independently, initiate activities, and follow through to completion.
- Leverage data and insights to shape and defend a point of view aligned to Audit’s business needs.
- Communicate proficiently with both technical staff and business leaders, articulating complex ideas, strategies, and recommendations in terms each audience can act on.
Required Qualifications
- 8+ years of professional software engineering experience developing, implementing, and supporting enterprise applications and services.
- 5+ years of professional Python development experience, with a strong track record of building enterprise-grade applications and services.
- Demonstrated ability to write clean, maintainable, modular, testable, and well-structured Python code designed for long-term support and scalability — production-grade software that does not create technical debt.
- 1+ years of hands-on experience designing, developing, and productionizing AI agents or AI-assisted workflows using a major AI platform or model provider such as Microsoft, Anthropic, or OpenAI.
- Hands-on experience developing agents using a Skills-based architecture, including authoring and maintaining Skills definitions, markdown-based instructions, supporting resources, and executable code used by agents.
- Experience testing and evaluating AI systems, including automated evaluators, representative test cases, regression testing, and analysis of agent failure modes.
- Demonstrated ability to embrace and advocate for AI-assisted software engineering practices, including the effective use of coding assistants (e.g. Claude Code, Codex, GitHub Copilot), automated testing, and design-time AI tools to accelerate solution design, improve quality, and enhance developer productivity.
- Demonstrated project management experience — scoping, planning, and managing technical projects/initiatives end-to-end in a fast-paced, cross-functional environment — with a strong track record of proactive, independent problem-solving.
- Experience providing technical leadership to software engineering teams, including mentoring engineers, conducting code and design reviews, and establishing engineering practices and standards.
- Experience designing and deploying AI/ML solutions on major cloud platforms (AWS, Azure, and/or GCP), including the use of managed AI services, cloud-native integration patterns, scalable data architectures, and secure, enterprise-grade deployments. Azure experience preferred
- Experience with Azure DevOps — including building and maintaining CI/CD pipelines — or a similar platform, and Git-based version control.
- Excellent verbal and written communication skills, with the ability to explain complex technical work to non-technical business leaders and translate complex requirements into practical technical solutions alongside product managers, architects, and business stakeholders.
Preferred Qualifications
- Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent professional experience.
- Experience developing software or AI solutions for Audit, accounting, public accounting, financial services, or another regulated industry.
- Proficiency with modern AI/ML frameworks, tools, and agent platforms (e.g. MCP, A2A, Microsoft Foundry, OpenAI Agent SDK, Microsoft Agent Framework, Claude SDK, LangGraph, LangChain, LlamaIndex, Microsoft Copilot Studio, or equivalent). Knowledge of retrieval-augmented generation (RAG), vector databases, embeddings, and techniques for grounding model outputs in enterprise data.
- Experience delivering solutions with the Microsoft .NET stack, C#, and SQL Server.
- Experience helping establish or scale an engineering capability, including defining development standards, reusable patterns, and practices that support a growing team and portfolio of production solutions.
- Consulting, product management, investment banking, or startup experience that has developed strong commercial and product instincts.
- Prior experience as a forward deployed engineer or similar embedded technical role, partnering directly with subject matter experts to design and build workflow software without a separate product-owner layer.
- Demonstrated passion for sharing what you learn, whether by offering cross-training opportunities, giving internal team “lightning talks,” or by writing detailed comments on tickets when you close them.
- Demonstrated curiosity and continuous learning mindset in Generative AI and applied machine learning, with an interest in staying current on emerging models, architectures, and techniques, including engaging with original research, technical papers, and pre-release concepts before they are broadly commercialized
#Hybrid
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 $102,400.00 - $204,100.00 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 provides professional services through two separate entities: Crowe LLP delivers audit and attest services, and Crowe Advisory LLC provides tax, advisory, consulting, and other nonattest services.
The Crowe Global network consists of more than 300 independent accounting and advisory services firms in more than 150 countries around the globe, making it one of the largest accounting networks in the world*. As independent members of Crowe Global, Crowe LLP and Crowe Advisory LLC serve clients worldwide. (*As of July 2026)
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, 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.
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 $102K-$204K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($153K) sits 28% below the category median. Disclosed range: $102K to $204K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Crowe AI Hiring
Crowe has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Livingston, NJ, US, Chicago, IL, US, New York, NY. Compensation range: $100K - $204K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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