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About This Role
Job Summary:
The role is built as the counterpart of the Technology Transformation Leader, who owns tools, platforms, governance, and delivery of the firm\-wide results dashboards. Technology and people are the two halves of one model; this role leads the human half and owns the business measurement of both. It does not sell directly to clients: offerings built on the Firm's transformation go to market through the established practices. As the champions network grows toward hundreds, each practice may add an enablement counterpart coordinated by this role, and scope extends to BDO's member firms abroad.
This role reports directly to the AI Enablement Leader and Global BDO Digital Leader. Works in lockstep with the Technology Transformation Leader and closely with the firm's organizational\-development leader for this program, who owns delivery of people\-development activities in the Chief People Officer's organization; practice enablement counterparts, as appointed, hold a dotted line to this role.
This individual will be a change leader who executes: able to hold a practice\-leader revenue conversation in the morning, then run the cohort calendar, the office\-hours rotation, and the recognition system with discipline in the afternoon. The experience bar: the last programs taken from design to measured adoption at scale.
Job Duties:
- Owns people enablement end to end: the AI Champions Network (launching with 50 champions across all five practices), the firm\-wide training program scaled through champion\-coached cohorts, weekly office hours, each practice's self\-study catalog, and the recognition and motivation system
- Works through the firm's people\-development organization, which owns delivery, on the AI enablement partner's frameworks.
- Leads change management and the communications that make change visible, working with Marketing and the practices so the stories that spark change circulate, the people behind them are recognized by name, and colleagues see and feel the transformation
- Owns the revenue bridge on the people side: documents the transformation as it happens (BDO adopts AI first, and the playbook becomes client offerings) and holds the relationships with Advisory, Tax, and Assurance that turn enablement assets into sellable offerings
- Holds the partner and vendor relationships on the people side, for enablement and training: the firm's AI enablement partner, the communications partner, the learning and continuing\-education bodies, and the AI platform vendors the firm's people learn and build on, including Microsoft, Anthropic, OpenAI, and Google. Technical evaluation, procurement, and platform operations stay with the technology organization
- Runs the measurement and accountability machinery: the business metrics definitions behind the firm dashboards, the economics and financial analysis of the transformation (value estimated conservatively, cost per person and per agent), the program scorecard, the open\-items queue, and the day\-45 and day\-90 readout cadence, driving each item to resolution with its owner
- Drives organizational redesign with the Chief People Officer's organization: as hybrid teams of people and AI agents become the norm, designs what makes the new way of working permanent (compensation, job descriptions, career paths, hiring profiles, reward systems), always with the CPO's sign\-off on anything touching pay
- Fluency in managing a hybrid workforce of people and AI agents
- Other duties as required
Qualifications, Knowledge, Skills, and Abilities:
Education:
- Bachelor's degree, required
- Master's degree in Business Administration or Organizational Change, preferred
Experience:
- Twelve (12\) or more years leading transformation, change management, people development, or practice operations at scale, in your own organization or for clients, including senior leadership (principal, partner, managing director, or equivalent) in professional services or a comparable complex, matrixed organization, required
- Experience leading programs from design through measured adoption at scale, including presentation of quantitative results and demonstrated success influencing thousands of professionals across practices and geographies without direct reporting authority, required
- Experience building and managing a working team of no\-code AI agents using platforms such as Microsoft 365 Copilot and agent tools from Anthropic, OpenAI, and Google, with demonstrated ability to present live examples of agent responsibilities, ongoing management practices, outcomes delivered, and lessons learned from operating a hybrid human and AI team, required
- Ranges across audiences, from client rooms to the front line: the same material must land with a practice leader, a client executive, and a first\-year cohort, required
- Experience working in a partnership\-led environment, preferred
- Experience working in a regulated environment, preferred
- Experience converting internal capabilities into market\-facing offerings, preferred
- Experience designing compensation structures with Human Resources, preferred
- Experience designing career structures with Human Resources, preferred
- Experience applying economics, financial analysis, or behavioral economics to measurement frameworks and business decision\-making, preferred
- Experience building AI agents through direct integration with AI model application programming interfaces (APIs), preferred
Certifications:
- N/A
Licensure:
- N/A
Software:
- Hands\-on, daily use of enterprise AI platforms, copilots, and agent\-building tools, required
- AI co\-working tools and command\-line AI assistants, required
Other Knowledge, Skills, and Abilities:
- Ability to lead through influence across practices and geographies
- Ability to communicate credibly and effectively with executive leadership, revenue\-generating stakeholders, and early\-career professionals
- Ability to translate strategy into execution plans, measurable outcomes, and scorecards that track progress
- Ability to build visible and sustainable change through recognition, storytelling, and reinforcement practices
- Ability to demonstrate genuine care for the employee experience during change, including confidence, agency, and meaning in work
Broad working knowledge of the AI agent development market, with the ability to evaluate options credibly, guide firm decision\-making, and communicate effectively with vendors and builders
*
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: $250,000 \- $350,000
Maryland Range: $250,000 \- $350,000
NYC/Long Island/Westchester Range: $250,000 \- $350,000
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 $250K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($300K) sits 40% above the category median. Disclosed range: $250K to $350K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BDO AI Hiring
BDO has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $350K - $350K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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