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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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Job Description:
Baker Tilly is seeking a VP of AI Engineering to lead the firm’s Applied AI and Data & Intelligence engineering capabilities as one integrated organization. This leader will oversee the Agent Factory, which builds the firm’s AI agents, agentic experiences, and evaluation capabilities, as well as Data & Intelligence, which builds Compass, the BT Intelligence Platform, including the ontology, knowledge and judgment graphs, and product data estate that power the firm’s agentic solutions.
The VP of AI Engineering will establish and maintain the engineering standards, delivery practices, and talent strategy across both areas while ensuring the organization delivers high-quality, production-ready AI products. This leader will build and develop the next phase of engineering teams, including forward-deployed AI engineers, data and knowledge-graph engineers, machine-learning engineers, and AI evaluation talent across the U.S. and India.
A key responsibility of this role will be leading the deliberate transition of engineering capabilities from expert partners to Baker Tilly teams, establishing the processes, standards, patterns, and internal expertise necessary for the firm to own and scale its AI capabilities over time.
This is a player-coach leadership role requiring both executive-level engineering leadership and current technical credibility. The VP of AI Engineering will remain close enough to the work to evaluate agent architectures, evaluation frameworks, engineering estimates, and delivery decisions while building the organization and operating model needed to scale. The role will partner closely with Platform & Production Engineering, Architecture, AI Product Management, Design, subject matter experts, and the India engineering organization to operate as one engineering system.
Responsibilities
- Build and lead an integrated engineering organization across the Agent Factory and Data & Intelligence, establishing engineering standards, delivery systems, culture, and a consistent talent bar.
- Hire, develop, and retain high-performing engineering teams across the U.S. and India, including forward-deployed AI engineers, data and knowledge-graph engineers, machine-learning engineers, and AI evaluation talent.
- Establish and maintain engineering hiring standards and structured selection practices across the organization.
- Design and lead a product pod model that brings together U.S. and India engineers, product managers, subject matter experts, and other partners as integrated teams.
- Partner with the VP, Engineering – India to grow the India engineering organization while maintaining consistent engineering standards and practices across geographies.
- Own engineering delivery across the APG-built portfolio, including agents, Compass product experiences, the intelligence layer, product data estate, and the evaluation evidence supporting release decisions.
- Establish an operating cadence across product teams, engineering partners, and the India organization to drive accountability, visibility, and delivery quality.
- Maintain a high bar for engineering and release quality, ensuring evaluation baselines and evidence inform release decisions.
- Oversee engineering dependencies across the agentic and data layers and ensure release candidates transition effectively to Platform & Production Engineering through established interfaces and release processes.
- Lead the transition of engineering capabilities from expert partners to Baker Tilly teams through a structured shadow, pair, lead, and own model with defined milestones and ownership gates.
- Translate partner-developed capabilities into Baker Tilly-owned patterns, runbooks, engineering practices, and internal expertise.
- Manage partner engineering delivery against defined outcomes and technical standards as capabilities transition in-house.
- Partner closely with Platform & Production Engineering to maintain a clear and effective interface between product development and the path to production.
- Partner with the Chief Architect on technical design, architecture standards, and the interfaces between the agentic, intelligence, and platform layers.
- Collaborate with AI Product Management to translate product definition and acceptance criteria into engineering execution, with evaluation results informing product and release decisions.
- Partner with Design and subject matter experts to ensure agentic products reflect high-quality user experiences and practitioner expertise.
- Provide leadership and mentorship to the Head of Agent Engineering and Head of Data & AI Platform and ensure alignment across both engineering pillars.
- Serve as a senior engineering and delivery advisor to the CAITO, providing clear visibility into delivery progress, risks, dependencies, and organizational needs.
- Represent APG engineering with executive stakeholders and communicate complex engineering topics, trade-offs, risks, and delivery status clearly and effectively.
- Engage in special projects and perform other duties as assigned.
Qualifications
- Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience; advanced degree preferred.
- 15+ years of software and product engineering experience, including significant senior leadership experience within product engineering organizations.
- Demonstrated experience building an engineering organization from an early or founding stage and successfully delivering AI products through that organization.
- Recent hands-on technical credibility with LLM-based and agentic systems, with the ability to effectively evaluate agent architectures, evaluation suites, technical approaches, engineering estimates, and trade-offs.
- Strong knowledge of agentic AI product engineering, including LLM systems, evaluation-gated delivery, context and memory management, and tool use.
- Experience with data and knowledge systems, including lakehouse environments, knowledge graphs, retrieval systems, and evaluation-driven approaches to retrieval quality.
- Deep engineering management expertise, including establishing technical standards, hiring practices, delivery systems, and dependency-management processes.
- Experience leading engineering organizations that build products and services on platforms owned by separate teams, with demonstrated ability to establish effective interfaces and operating models across organizational boundaries.
- Experience leading distributed U.S. and India engineering organizations as an integrated engineering system.
- Experience leading complex engineering programs across vendors, product teams, partners, and multiple time zones.
- Experience working within forward-deployed, client-embedded, or partner-supported engineering models, including transitioning engineering capabilities from external partners to internal teams.
- Demonstrated ability to develop internal engineering capabilities, practices, and talent while reducing long-term reliance on external delivery partners.
- Strong executive communication skills, with the ability to communicate delivery status, risks, dependencies, and technical considerations clearly and proactively.
- Demonstrated ability to use AI and agentic development tools as a force multiplier in engineering and leadership work.
- Ability to balance hands-on technical depth with organizational leadership, talent development, delivery accountability, and executive stakeholder management.
- No certifications or licenses required.
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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