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About This Role
Work with a Top 20 CPA and advisory firm that Accounts for Anything. Aprio has 40 U.S. office locations, as well as international office locations and more than 3,200 team members that speak 60\+ languages across the globe. By bringing together proven expertise, deep understanding, and strategic foresight for fast\-growing industries, Aprio ensures clients are prepared for wherever life or business may take them. Discover a top\-rated culture, vast growth opportunities and your next big career move with Aprio.
Join Aprio's Advisory team and you will help clients maximize their opportunities. Aprio Advisory Group, LLC is a progressive, fast\-growing firm looking for a Senior Associate to join their dynamic team.
We are looking for an AI Strategy Senior Associate to join our Data \& AI practice within Aprio's Technology Advisory group. In this role, you will help clients move from Data and AI ambition to applied, business\-driven solutions: assessing where AI can create value, shaping the roadmap to get there, and building the proof\-of\-concept solutions and demos that demonstrate it. You will work at the intersection of business strategy, business analysis, and technical execution, partnering with clients across our transformation journey, from Assess \& Strategize and Integrate \& Modernize through Optimize \& Build and Deploy \& Scale. This role suits an early\-career professional with applied AI experience who wants to stay hands\-on with solution development while growing into client\-facing advisory work.
*This position is a heavy in person customer facing role that requires travel and interaction with customers across the US.*
### Responsibilities:
AI strategy and assessment
- Conduct AI readiness assessments and data maturity evaluations that help clients understand their current capabilities and see where AI and automation can support value creation.
- Translate business challenges into prioritized Data and AI use cases and help shape transformation roadmaps that align technology investments with client objectives.
- Research emerging AI capabilities (large language models, machine learning, data management, generative AI, and agentic AI) and explain them in clear, accessible terms for non\-technical stakeholders.
Solution design and delivery
- Design and prototype agentic AI solutions across our core solution types: document processing and workflow automation; automated communication and prediction analytics; system integration and data synchronization; real\-time external data validation; and business intelligence with work prioritization.
- Build proof\-of\-concept solutions and demos that help business teams explore the potential of Data and AI and analytics approaches, applying a structured approach to comparing models and options so recommendations are well\-reasoned.
- Support SOW\-based engagement delivery across discovery, kickoff, weekly status, and final delivery, contributing alongside the delivery team.
Responsible AI and governance
- Help clients adopt AI responsibly, sustainably, and compliantly, building appropriate safeguards and monitoring into solution design.
- Contribute to AI and data governance frameworks that promote responsible, sustainable, and compliant use of AI.
Client partnership
- Act as a bridge between technical and business domains, the role our team calls a Data Translator / Business Engineer, turning technical detail into business value.
- Partner with business stakeholders to understand their needs and help translate them into data\- and AI\-driven solutions.
- Present concepts and findings to business audiences in clear language, including through workshops and working sessions.
### Required Qualifications:
- Bachelor’s or Master’s degree in Computer science, Data Science, Engineering, or a related quantitative field.
- 3\+ years of applied AI, data management, or machine learning experience. Strong internship, research, or academic project work is welcome in place of full\-time experience.
- Hands\-on experience building AI or machine learning solutions, including work with large language models, natural language processing (NLP), or agentic AI approaches.
- Strong programming skills in Python, with experience using modern AI development tools.
- Ability to move between technical detail and business value, and to communicate clearly with non\-technical stakeholders.
- Strong analytical and problem\-solving skills, with a structured, well\-tested approach to AI work.
### Preferred Qualifications:
- Experience with agentic or autonomous agent design and AI\-assisted development workflows.
- Exposure to taking AI or machine learning solutions beyond a notebook toward deployment, for example through a major cloud platform, containerization, or basic monitoring, gained via internships, research, or coursework.
- Exposure to responsible AI practices such as model evaluation, explainability, and fairness or drift checks.
- Familiarity with business intelligence tools (for example, Power BI or Tableau) and modern data platforms (for example, Snowflake, Databricks, or Spark).
- Exposure to client business systems and ERPs (NetSuite, QBO/IES, Acumatica, or Sage) is a plus.
- Interest in client\-facing advisory work and a helpful, customer\-oriented approach.
Why work for Aprio:
Whether you are just starting out, looking to advance into management or searching for your next leadership role, Aprio offers an opportunity to grow with a future\-focused, innovative firm.
Perks/Benefits we offer for full\-time team members:
- Medical, Dental, and Vision Insurance on the first day of employment
- Flexible Spending Account and Dependent Care Account
- 401k with Profit Sharing
- 9\+ holidays and discretionary time off structure
- Parental Leave – coverage for both primary and secondary caregivers
- Tuition Assistance Program and CPA support program with cash incentive upon completion
- Discretionary incentive compensation based on firm, group and individual performance
- Incentive compensation related to origination of new client sales
- Top rated wellness program
- Flexible working environment including remote and hybrid options
What’s in it for you:
- Working with an industry leader: Be part of a high\-growth firm that is passionate for what’s next.
- An awesome culture: Thirty\-one fundamental behaviors guide our culture every day ensuring we always deliver an exceptional team\-member and client experience. We call it the Aprio Way. This shared mindset creates lasting relationships between team members and with clients.
- A great team: Work with a high\-energy, passionate, caring and ambitious team of professionals in a collaborative culture.
- Entrepreneurship: Have the freedom to innovate and bring your ideas to help us grow to become the CPA firm of choice nationally.
- Growth opportunities: Grow professionally in an environment that fosters continuous learning and advancement.
- Competitive compensation: You will be rewarded with competitive compensation, industry\-leading benefits and a flexible work environment to enjoy work/life balance.
EQUAL OPPORTUNITY EMPLOYER
Aprio is an Equal Opportunity Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race; color; religion; national origin; sex; pregnancy; sexual orientation; gender identity and/or expression; age; disability; genetic information, citizenship status; military service obligations or any other category protected by applicable federal, state, or local law.
Aprio, LLP and Aprio Advisory Group, LLC, operate in an alternative business structure, with Aprio Advisory Group, LLC providing non\-attest tax and consulting services, and Aprio, LLP providing CPA firm services.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Aprio, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Aprio AI Hiring
Aprio has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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