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Overview
Baker Tilly is a leading advisory, tax and assurance firm, providing clients with a genuine coast-to-coast and global advantage in major regions of the U.S. and in many of the world’s leading financial centers – New York, London, San Francisco, Los Angeles, Chicago and Boston. Baker Tilly Advisory Group, LP and Baker Tilly US, LLP (Baker Tilly) provide professional services through an alternative practice structure in accordance with the AICPA Code of Professional Conduct and applicable laws, regulations and professional standards. Baker Tilly US, LLP is a licensed independent CPA firm that provides attest services to its clients. Baker Tilly Advisory Group, LP and its subsidiary entities provide tax and business advisory services to their clients. Baker Tilly Advisory Group, LP and its subsidiary entities are not licensed CPA firms.
Baker Tilly Advisory Group, LP and Baker Tilly US, LLP, trading as Baker Tilly, are independent members of Baker Tilly International, a worldwide network of independent accounting and business advisory firms in 141 territories, with 43,000 professionals and a combined worldwide revenue of $5.2 billion. Visit bakertilly.com or join the conversation on LinkedIn, Facebook and Instagram.
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Baker Tilly is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability or protected veteran status, gender identity, sexual orientation, or any other legally protected basis, in accordance with applicable federal, state or local law.
Any unsolicited resumes submitted through our website or to Baker Tilly Advisory Group, LP, employee e-mail accounts are considered property of Baker Tilly Advisory Group, LP, and are not subject to payment of agency fees. In order to be an authorized recruitment agency ("search firm") for Baker Tilly Advisory Group, LP, there must be a formal written agreement in place and the agency must be invited, by Baker Tilly's Talent Attraction team, to submit candidates for review via our applicant tracking system.
Job Description:
Baker Tilly is seeking a Vice President of AI Product Management to establish and lead the product management function for our AI products and platforms. This leader will oversee product strategy and execution across three key areas: AI Agents and Agent Factory, Compass / Intelligence Platform, and the broader AI Platform.
Working closely with senior practitioners, engineering leaders, design, and executive leadership, the VP will translate business and practitioner needs into clear product strategies, roadmaps, and specifications that enable teams to build and launch high-impact AI solutions. This leader will own the product lifecycle from definition and prioritization through launch, adoption, and continuous improvement.
The VP will also build and develop a high-performing product management organization, establish product standards and operating rhythms, and help shape how Baker Tilly develops and scales AI-enabled products across the firm.
Responsibilities
- Own the product strategy, roadmap, and portfolio across AI Agents and Agent Factory, Compass / Intelligence Platform, and the AI Platform, partnering with business and technology leaders to establish priorities and measure value.
- Establish and lead the product definition process, including product requirements, specifications, prioritization, and acceptance criteria informed by evaluation results and user needs.
- Translate practitioner and business needs into scalable, buildable product solutions while balancing user experience, technical feasibility, business value, and speed to market.
- Partner closely with engineering, architecture, design, and domain leaders throughout the product lifecycle to ensure alignment from product definition through delivery.
- Lead product launches and adoption strategies in partnership with service lines, including practitioner rollout, enablement, adoption measurement, and feedback loops that inform future product development.
- Establish consistent product management practices and operating rhythms across teams in the U.S. and India.
- Build and lead the AI product management organization, including hiring, developing, and coaching senior product management talent and establishing standards for product excellence.
- Partner closely with design leadership to ensure product definition, user experience, and acceptance criteria are developed cohesively.
- Develop and communicate the product narrative to senior and executive leadership, clearly articulating what is being built, why it matters, and the value it creates.
- Evaluate the performance and value of products across the portfolio, using data, adoption insights, and business outcomes to inform investment and prioritization decisions.
- Engage and participate in special projects or complete other duties as assigned.
Qualifications
- Bachelor’s degree in computer science, engineering, business, or a related field, or equivalent practical experience; advanced degree a plus.
- No certifications or licenses required.
- 15+ years of product management, including 5+ years leading product teams, with AI products shipped to production.
- Has built or re-founded a product management function — hired the PMs, set the standard, and made it stick.
- Exceptional written product definition craft — the PRD standard is the instrument of this organization, and the writing sample will be read.
- Has worked directly with domain experts and engineers simultaneously, and earned credibility with both in the same week.
- Technical fluency: reads architecture and evaluation results well enough to anchor acceptance criteria in them and to challenge an estimate.
- Has launched products into skeptical, expert user bases and won adoption; enterprise or B2B SaaS depth preferred.
- Has operated at executive scope — roadmap, portfolio, and narrative to senior leadership — without losing the craft.
- Demonstrates personal AI leverage: designs and builds with agentic tools as a force multiplier — the working standard for this team.
- AI product judgment: model-capability trends, human-in-the-loop design, trust and adoption dynamics.
- Evaluation literacy: baselines, regression, confidence — enough to write criteria engineers respect.
- Spec-driven development and AI-assisted product craft.
- Portfolio and prioritization discipline under ambiguity; value cases that survive finance.
- Professional-services domain empathy: how practitioners actually work, bill, review, and sign.
- Product organization building: hiring bars, PM career paths, definition cadences across time zones.
The pay range for this position is $250,000 to $400,000. This range represents a good-faith and reasonable estimate of the compensation the firm expects to pay for this position at the time of posting. Actual compensation will depend on a variety of relevant factors, including an applicant’s skills, experience, qualifications, professional certifications, work arrangements, geographic location, and other business and organizational needs. It is not typical for an individual to be hired at or near the top of the range, and compensation decisions depend on the facts and circumstances of each case. Baker Tilly offers a compensation and benefits package to eligible employees.
Salary Context
This $250K-$400K range is above the 75th percentile 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 Baker Tilly Canada, 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 in Demand for This Role
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. This role's midpoint ($325K) sits 53% above the category median. Disclosed range: $250K to $400K.
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.
Baker Tilly Canada AI Hiring
Baker Tilly Canada has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Irvine, CA, US, Walnut Creek, CA, US, San Diego, CA, US. Compensation range: $182K - $400K.
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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