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About This Role
We are the leading provider of professional services to the middle market globally, our purpose is to instill confidence in a world of change, empowering our clients and people to realize their full potential. Our exceptional people are the key to our unrivaled, culture and talent experience and our ability to be compelling to our clients. You’ll find an environment that inspires and empowers you to thrive both personally and professionally. There’s no one like you and that’s why there’s nowhere like RSM.
As a Manager in RSM’s Advanced Analytics and AI Consulting practice, you will lead client engagements that help organizations unlock business value from data, machine learning, and AI. You’ll work closely with cross\-functional consulting teams to design and deliver scalable AI and analytics solutions that enhance decision\-making, automate processes, and drive measurable business impact.
This is a client\-facing leadership role that requires both technical expertise and business acumen. You will guide project teams through the full lifecycle of analytics and AI solution delivery, from discovery and data preparation to modeling, deployment, and operationalization, while mentoring junior team members and collaborating with RSM’s national network of data and digital professionals.
Key Responsibilities
- Lead the design, development, and deployment of AI and machine learning models using Python, R, and/or Azure Machine Learning.
- Oversee the creation of data pipelines and analytics architectures leveraging Microsoft Fabric, Azure Data Factory, Azure Data Lake, Synapse, and Power BI.
- Partner with business and technical stakeholders to translate business objectives into data\-driven solutions and define measurable success criteria.
- Drive Generative AI and AI Agent projects, including solution design, architecture, and integration with enterprise data ecosystems.
- Conduct and review exploratory data analysis, feature engineering, and model evaluation to ensure performance and interpretability.
- Lead engagement delivery, ensuring quality, timeliness, and alignment with client expectations.
- Collaborate across regions and service lines to deliver integrated, cross\-disciplinary AI solutions.
- Mentor and coach analysts and consultants in data science methods, solution development, and client delivery best practices.
Basic Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Information Systems, Engineering, Statistics, Mathematics, or related field.
- 8\+ years of experience in data analytics, data science, or AI solution development.
- Proven experience designing and implementing end\-to\-end analytics and AI solutions using Azure technologies (Fabric, Data Factory, Synapse, Data Lake, Machine Learning).
- Proficiency with Python and/or R for data science and machine learning workflows.
- Experience with data visualization and storytelling, particularly using Power BI or equivalent tools.
- Strong understanding of modern data architectures, ML lifecycle management, and MLOps concepts.
- Excellent communication and presentation skills with the ability to convey technical concepts to business audiences.
- Demonstrated ability to lead teams, manage client relationships, and deliver complex projects on time and within scope.
- Strong curiosity and passion for solving complex business problems through AI and advanced analytics.
- Ability and willingness to travel to the US will be required, including for client meetings and conferences
Preferred Qualifications
- Experience with Azure OpenAI, Copilot Studio, or other LLM\-based solutions.
- Familiarity with Generative AI architectures, vector databases, and retrieval\-augmented generation (RAG) design patterns.
- Exposure to data governance, Responsible AI, and model risk management frameworks.
- Experience in cloud solution architecture, particularly within Microsoft Azure ecosystems.
- Advanced degree (MS) in a quantitative discipline.
At RSM, we offer a competitive benefits and compensation package for all our people. We offer flexibility in your schedule, empowering you to balance life’s demands, while also maintaining your ability to serve clients. Learn more about our total rewards at https://rsmus.com/careers/working\-at\-rsm/benefits.
All applicants will receive consideration for employment as RSM does not tolerate discrimination and/or harassment based on race; color; creed; sincerely held religious beliefs, practices or observances; sex (including pregnancy or disabilities related to nursing); gender; sexual orientation; HIV Status; national origin; ancestry; familial or marital status; age; physical or mental disability; citizenship; political affiliation; medical condition (including family and medical leave); domestic violence victim status; past, current or prospective service in the US uniformed service; US Military/Veteran status; pre\-disposing genetic characteristics or any other characteristic protected under applicable federal, state or local law.
Accommodation for applicants with disabilities is available upon request in connection with the recruitment process and/or employment/partnership. RSM is committed to providing equal opportunity and reasonable accommodation for people with disabilities. If you require a reasonable accommodation to complete an application, interview, or otherwise participate in the recruiting process, please call us at 800\-274\-3978 or send us an email at careers@rsmus.com.
RSM does not intend to hire entry level candidates who will require sponsorship now OR in the future (i.e. F\-1 visa holders). If you are a recent U.S. college / university graduate possessing 1\-2 years of progressive and relevant work experience in a same or similar role to the one for which you are applying, excluding internships, you may be eligible for hire as an experienced associate.
RSM will consider for employment qualified applicants with arrest or conviction records. For those living in California or applying to a position in California, please click here for additional information.
At RSM, an employee’s pay at any point in their career is intended to reflect their experiences, performance, and skills for their current role. The salary range (or starting rate for interns and associates) for this role represents numerous factors considered in the hiring decisions including, but not limited to, education, skills, work experience, certifications, location, etc. As such, pay for the successful candidate(s) could fall anywhere within the stated range.
Compensation Range: $112,100 \- $225,500
Individuals selected for this role will be eligible for a discretionary bonus based on firm and individual performance.
Salary Context
This $112K-$225K 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 RSM, 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. Mid-level AI roles across all categories have a median of $131,300. Disclosed range: $112K to $225K.
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.
RSM AI Hiring
RSM has 29 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Charlotte, NC, US, Tampa, FL, US, New York, NY, US. Compensation range: $203K - $269K.
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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