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About This Role
Job Summary:
The Senior AI Product Owner at BDO will serve as the subject matter expert and primary advocate for AI products within the firm. This role involves managing the product lifecycle for existing AI products, from user research and data analysis to feature release and performance monitoring, ensuring alignment with business objectives and customer needs. The AI Product Owner will drive the adoption and growth of AI products, fostering innovation and maintaining a robust feedback loop to continuously enhance product offerings and solutions. Key responsibilities include prioritizing the product backlog, defining success metrics, and collaborating with teams to promote the usage of AI products across the organization.
Job Duties:
- Acts as a subject matter expert for AI products at BDO, capable of demonstrating capabilities to users and groups across the firm
- Conducts workshops to gather product feedback from various groups across the firm, ensuring alignment with customer needs and firm strategy
- Acts as an advocate and evangelist for AI products within the firm, collaborating with teams to promote their value and encouraging widespread adoption
- Establishes and maintains a robust feedback loop with users to continuously gather insights and drive product improvements.
- Defines and monitors key performance indicators (KPI’s) and success metrics for AI products.
- Conducts user research, data analysis, and collects customer feedback to inform product decisions and iterations
- Conducts market research to identify trends and opportunities for AI product enhancements
- Monitors the performance and usage of AI products, identifying areas for improvement and driving initiatives to enhance user adoption
- Oversees the creation of product roadmaps for AI products and plan feature releases based on business need
- Ensures compliance with technical and ethical standards in AI product development
- Writes user stories and prioritize the product backlog, ensuring the team delivers the most impactful AI features while maintaining technical and ethical integrity
- Engages in team planning, commit to sprints based on average velocity, and integrate testing frequently to address problems early
- Oversees the timely release of new features, including writing release notes and ensuring the release team is aligned and prepared as needed
- Fosters a culture of innovation and experimentation within the product team, encouraging the exploration of new ideas and technologies to enhance the AI product
- Maintains comprehensive documentation of product features, updates, and user feedback, and provide regular reports to senior management on product performance and adoption metrics
- Creates and prioritizes work for the AI Engineering team, ensuring a steady flow of tasks
- Helps to cultivate a product mindset among the group
Other duties as required *
Qualifications, Knowledge, Skills, and Abilities:
Education:
- High School Diploma or equivalent, required
- Bachelor’s degree, preferred; focus in business administration, accounting, computer or information science, or equivalent work experience, preferred
Experience:
- Eight (8) or more years of experience as a Product Owner or Business Analyst in an Agile Environment, required
- Broad business experience within a professional services environment, preferred
- Experience managing Artificial Intelligence tools, preferred
License(s)/Certification(s):
- Certified Scrum Product Owner Certification, preferred
Software:
- Experience in the following, preferred: + ChatGPT, Microsoft 365 Copilot, or other AI tools + Microsoft Teams, Word, Excel, Outlook, PowerPoint, OneNote, OneDrive, PowerBI, Visio + Azure DevOps or Jira + Wireframing software tools
Other Knowledge, Skills & Abilities:
- A strong understanding of AI and machine learning concepts, technologies, and trends
- Knowledge of software development processes and agile methodologies
- Proficiency in defining and monitoring key performance indicators (KPIs) and success metrics
- Strong problem-solving skills with the ability to think critically and make data-driven decisions
- Ability to deliver innovative solutions
- A passion for quality software crafted with customer-centric problem-solving skills and design thinking
- Understanding of ethical implications and considerations in AI development and deployment
- Ability to create and lead high-performing cross-functional teams, with team members not directly under his/her/their supervision
- Strong organizational skills with the ability to manage multiple priorities and projects simultaneously
- Recognized as a powerful and effective communicator
- Strong presentation skills for demonstrating product capabilities and conducting workshops
- Known for high-performance listening and reading comprehension skills, as well as excellent verbal and written communication skills
- Ability to explain technologies to business leaders, and business concepts to technologists
- Ability to handle conflict-resolution; with an ability to approach a problem from various viewpoints, and achieve consensus
- Ability to learn from experience and apply that learning to perform successfully in new situations
- Resiliency in ambiguous situations
- Comfort with the concepts of transparency, inspection, and adaptation
- Focused on relentless improvement
- Adept at balancing team and individual responsibilities
- Possesses business acumen and interpersonal skills to influence and affect business, culture, and technology changes in a dynamic and complex operating environment
- Able to achieve common goals while working ethically and with integrity
Ability to work beyond standard business hours and travel, as needed *
Individual salaries that are offered to a candidate are determined after consideration of numerous factors including but not limited to the candidate’s qualifications, experience, skills, and geography.
National Range: $130,000 – $155,000
Maryland Range: $130,000 – $155,000
NYC/Long Island/Westchester Range: $130,000 – $155,000
At BDO, how we show up matters. We build strong relationships by supporting one another, our clients, and our communities with care, curiosity, and a commitment to helping one another grow and succeed. Here, you’ll find meaningful work, leaders invested in your success, and opportunities to build a career around what matters most to you.
Our purpose is to be the people our clients count on to grow with confidence and achieve what matters most. Our values guide how we bring that purpose to life each day. Together, they shape how we work with one another, serve our clients, and create meaningful impact.
BDO provides assurance, tax, and advisory services to clients across the U.S. and around the world. No matter your role, you’ll be part of a team helping clients navigate complexity and move forward with clarity.
We are proud to be an ESOP company, offering participants a stake in the firm’s success through beneficial ownership and a unique opportunity to enhance their financial well-being. As a qualified retirement plan, the ESOP is a meaningful addition to our comprehensive compensation and Total Rewards benefits* offerings. It also reinforces an ownership mindset that strengthens our connection to one another, our clients, and the future we’re building together.
Learn more about our benefits: BDO Total Rewards encompass more than traditional benefits. Click here to find out more !
*Benefits may be subject to eligibility requirements.
Equal Opportunity Employer, including disability/vets
Click here to find out more !
Salary Context
This $130K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At BDO, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($142K) sits 33% below the category median. Disclosed range: $130K to $155K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
BDO AI Hiring
BDO has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Raleigh, NC, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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