Interested in this AI/ML Engineer role at Baker Tilly Canada?
Apply Now →Skills & Technologies
About This Role
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.
Please discuss the work location status with your Baker Tilly talent acquisition professional to understand the requirements for an opportunity you are exploring.
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:
Role overview
Baker Tilly is seeking a senior consulting leader with deep applied AI expertise to define and lead the next phase of AI activation across our national consulting business. The firm has already established enterprise AI infrastructure and completed significant use-case identification. The opportunity now is to translate that foundation into a clear vision, an actionable portfolio of priorities, and measurable changes in how consulting services are delivered.
This is a business leadership role—not ownership of the underlying AI technology platform or technical delivery organization. The Director must bring enough technical depth to engage credibly with architects, engineers, data professionals, security teams, and platform owners; critically evaluate proposed approaches; challenge assumptions, estimates, constraints, and tradeoffs; and direct technical requests toward the business outcomes Consulting requires. The role will partner closely with the team responsible for OpenAI Enterprise (Anthropic and other tools in consideration), ensuring that technical decisions reflect real consulting workflows, economics, priorities, and adoption needs.
The successful candidate will combine consulting-business fluency, deep applied understanding of modern AI, strategic vision, commercial acumen, rigorous critical thinking, and strong executive influence. They will help practice leaders distinguish meaningful advances from market noise, envision new delivery models, understand revenue and margin implications, and prepare the consulting workforce for sustained change. They must also be comfortable constructively challenging technical teams when recommendations are insufficiently grounded in business value, user needs, practicality, or scale.
Key responsibilities
Set the vision and activation strategy
- Define and communicate a compelling vision for how AI should reshape Baker Tilly Consulting over the near, medium, and longer term.
- Translate firm strategy, market developments, and identified use cases into a prioritized Consulting AI roadmap with clear sequencing, dependencies, investment needs, and success measures.
- Advise consulting executives and practice leaders on where to experiment, scale, pause, or redirect effort based on business value, feasibility, risk, and organizational readiness.
- Maintain a strong external perspective on advances in generative AI, agentic systems, knowledge technologies, and emerging delivery models, separating durable signals from short-lived hype.
Reimagine consulting services and economics
- Partner with practice leaders and subject-matter experts to redesign service delivery, workflows, team structures, and client-value propositions around AI-enabled ways of working.
- Identify opportunities beyond labor efficiency, including improved quality and consistency, just-in-time staff training, stronger knowledge retention and dissemination, accelerated onboarding, enhanced judgment, and scalable access to expertise.
- Evaluate the implications of AI-enabled delivery for pricing, revenue growth, capacity, utilization, leverage models, profitability, intellectual property, and competitive differentiation.
- Develop business cases and decision frameworks that connect AI investments to measurable service-line and firm outcomes.
- Help convert high-value concepts into repeatable methods, accelerators, playbooks, and operating capabilities that can be adopted across practices.
Direct business-to-technology translation and challenge
- Serve as the primary Consulting business counterpart to the teams that manage enterprise AI platforms, data, integrations, security, architecture, and technical delivery.
- Convert consulting opportunities into clear, well-structured technical and capability requests, including business requirements, workflow context, priorities, use cases, data needs, integration needs, adoption expectations, constraints, and measurable outcomes.
- Apply sufficient technical depth to assess proposed architectures and delivery approaches, ask incisive questions, surface hidden assumptions, evaluate tradeoffs, and challenge recommendations that do not adequately address consulting-business needs.
- Hold technology partners accountable for translating technical possibilities and limitations into clear business implications, options, risks, costs, dependencies, and decisions.
- Coordinate with technology, innovation, risk, legal, information security, learning, and other enterprise functions to advance initiatives with appropriate controls and accountability.
Drive adoption and workforce readiness
- Align the Consulting AI strategy with the firm’s broader workforce upskilling, learning, talent, and change strategies.
- Define role-based AI capability expectations for consulting professionals, managers, leaders, specialists, and AI champions.
- Guide the development of practical enablement approaches, including embedded learning, workflow-based training, communities of practice, reusable knowledge assets, office hours, and leader communications.
- Build and mobilize a network of practice leaders, implementation leads, power users, and advocates who can translate the strategy into local action.
- Promote responsible experimentation while reinforcing appropriate human review, professional judgment, confidentiality, quality, and risk management.
Create operating discipline and measurable progress
- Establish a practical intake, prioritization, pilot, measurement, and scale process for Consulting AI initiatives.
- Define scorecards and KPIs that measure adoption, service-delivery impact, quality, workforce capability, revenue contribution, margin effects, and progress from pilot to scaled use.
- Provide clear, decision-oriented updates to Consulting leadership on progress, barriers, risks, investment needs, and business outcomes.
- Capture lessons learned and success stories, and use them to accelerate adoption across service lines without creating unnecessary bureaucracy.
Leadership profile
- Visionary but pragmatic: able to imagine materially different ways of delivering consulting services and convert them into executable priorities.
- Commercially minded: understands how professional services generate revenue and profit, and how changes in effort, leverage, pricing, quality, and speed affect economics.
- AI-fluent and technically credible: possesses deep applied knowledge of generative AI, agents, retrieval, data, integrations, model capabilities, limitations, and responsible deployment practices—sufficient to engage technical specialists, evaluate proposed solutions, and challenge weak reasoning without serving as the technical delivery owner.
- Business-to-technology director: can turn business strategy and consulting workflows into precise technical requests, define the outcomes and constraints that matter, and ensure technical teams remain aligned to commercial and operational value.
- Critical and independent thinker: tests assumptions, probes recommendations, compares alternatives, recognizes unsupported claims, and makes sound decisions when technical and business perspectives diverge.
- Influential and collaborative: builds trust with executives, practice leaders, practitioners, technologists, and control functions across a complex national firm.
- Disciplined and discerning: applies sound judgment to balance speed, innovation, professional standards, risk, and long-term scalability.
- Change-oriented: understands that sustainable AI activation requires shifts in behavior, skills, roles, incentives, and operating models—not merely access to tools.
What success looks like
- Consulting leaders share and act on a clear, credible vision for AI-enabled service delivery.
- A focused portfolio of priority initiatives moves from identified opportunity to adoption and scale.
- AI capability and technical delivery requests are precise, outcome-oriented, appropriately challenged, and aligned with Consulting priorities and enterprise platform strategy.
- Practices demonstrate measurable improvements in quality, speed, knowledge reuse, workforce readiness, revenue potential, and/or profit margin.
- Consulting professionals have practical, role-relevant ways to build AI capability and apply it responsibly in their work.
- Baker Tilly strengthens its competitive position by using AI to enhance expertise, judgment, client value, and scalability—not merely reduce effort.
Role scope and relationships
- Internal, non-client-facing leadership role supporting the national Consulting business.
- Works closely with Consulting executive leadership, practice leaders, AI implementation leads, Innovation, Applied AI/technology teams, Learning & Development, Talent, Legal, Risk, Information Security, and other enterprise functions.
- Does not own enterprise AI infrastructure, architecture, engineering, or technical operations; owns the Consulting business vision, activation agenda, prioritization, business requirements, and value realization—and provides informed direction and challenge to the technology owners.
Required qualifications
- Bachelor’s degree required; advanced degree or relevant professional credential preferred.
- 12+ years of experience in consulting, professional services, AI-enabled transformation, digital strategy, operating-model design, or a closely related field.
- Demonstrated experience applying AI to redesign business processes, knowledge work, service delivery, or professional workflows—not solely managing technology implementation.
- Strong understanding of consulting or professional-services economics, including pricing, utilization, leverage, capacity, revenue growth, and profitability.
- Proven ability to develop strategy, build business cases, prioritize a portfolio of opportunities, and move initiatives from concept through adoption and scale.
- Deep working knowledge of modern AI technologies and solution patterns, including generative AI, large language models, retrieval-augmented generation, agents, data and integration requirements, model limitations, evaluation, security, and human oversight.
- Demonstrated ability to engage architects, engineers, data professionals, and platform teams; frame effective technical requests; evaluate proposed approaches and estimates; identify gaps or weak assumptions; and constructively challenge technical recommendations.
- Exceptional critical-thinking and decision-making skills, including the ability to synthesize incomplete information, assess competing alternatives, distinguish genuine constraints from organizational preferences, and connect technical choices to business consequences.
- Executive-level communication, facilitation, and stakeholder-influence skills, including the ability to communicate complex AI and technology concepts in practical business terms.
- Experience partnering across business, technology, risk, legal, security, learning, and change-management functions.
- Strong judgment regarding responsible AI, data sensitivity, client confidentiality, intellectual property, human review, and quality control.
Preferred qualifications
- Leadership experience within a large, multi-service consulting or professional-services organization.
- Demonstrated experience with enterprise generative AI platforms such as OpenAI Enterprise, (Anthropic and other tools in consideration), custom GPTs, AI agents, knowledge retrieval, workflow automation, or related capabilities.
- Experience creating or scaling AI-enabled offerings, delivery methods, accelerators, centers of excellence, champion networks, or enterprise adoption programs.
- Experience leading broad workforce enablement, knowledge-management, or capability-building initiatives.
- A visible body of applied work, measurable outcomes, or thought leadership demonstrating the ability to turn AI possibilities into sustained business value.
The pay rate range for this job position is $214,100 to $405,900. Actual compensation is influenced by a variety of relevant factors including but not limited to applicant’s skills, prior experience, qualifications, degrees, professional certifications, work arrangements and geographic location. Baker Tilly offers a comprehensive compensation and benefits package to eligible employees.
Salary Context
This $214K-$405K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($310K) sits 46% above the category median. Disclosed range: $214K to $405K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Baker Tilly Canada AI Hiring
Baker Tilly Canada has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, Boston, MA, US, Santa Clara, CA, US. Compensation range: $405K - $550K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.