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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.
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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.
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Job Description:
Baker Tilly is seeking a Head of Data & AI Platform to serve as the firm’s principal Data Architect for AI products and lead the team responsible for the data and intelligence foundation that powers the firm’s AI product portfolio. This role will own the data layer end to end, including data architecture, data modeling, data engineering, and data science, as well as the intelligence capabilities that make up Compass, the BT Intelligence Platform, including the ontology, knowledge graph, judgment layer, product data estate, and supporting pipelines.
The Head of Data & AI Platform will define how data and practitioner knowledge are structured, connected, and transformed into reusable intelligence for the firm’s AI products. This leader will also establish the learning loop through which production signals, practitioner feedback, outcomes, and traces are used to continuously improve models, graphs, and retrieval quality.
This is a hands-on, player-coach role responsible for personally shaping the data architecture while building and leading a senior team across the U.S. and India. The role will establish standards and roadmaps for data, knowledge-graph, machine-learning, and data science capabilities and lead the transition of architecture and expertise from external partners to Baker Tilly teams.
The Head of Data & AI Platform will partner closely with Product Management, Architecture, Agent Engineering, Platform & Production Engineering, the enterprise data organization, subject matter experts, and engineering teams in India to ensure the firm’s AI products are built on a scalable, high-quality, and well-governed intelligence foundation.
Responsibilities
- Own the Baker Tilly Intelligence Layer, including the strategy, architecture, and roadmap for the ontology, knowledge graph, and judgment graph that determine what the firm’s AI products know and how practitioner judgment is captured and reused.
- Design and build ontology, knowledge-graph, and judgment-graph capabilities in partnership with engineering teams across the U.S. and India.
- Define how practitioner decisions, corrections, outcomes, and system traces are captured, curated, approved, and transformed into reusable intelligence.
- Establish retrieval semantics across graph- and vector-based approaches, with retrieval and grounding quality measured through evaluation.
- Own the data layer end to end, including data architecture, data modeling, product data models, the product data plane, extraction and integration pipelines, and data quality.
- Establish and maintain medallion architecture standards, data contracts, identity-resolution approaches, and data-quality practices for the product data estate.
- Build and maintain scalable pipelines for structured and unstructured data supporting the firm’s AI products and intelligence capabilities.
- Partner with the enterprise data organization to consume enterprise data through established data contracts, identity standards, client-identity keys, and systems-of-record boundaries.
- Own data science capabilities and establish the learning loop through which production signals, practitioner feedback, outcomes, and traces improve models, graphs, and retrieval.
- Lead corpus engineering programs, including document extraction, ground-truth development, and synthetic data generation, to support the Intelligence Layer.
- Establish approaches for measuring and improving retrieval quality, model performance, and drift through evaluation-driven practices.
- Build and lead the data, knowledge-graph, machine-learning, and data science teams across the U.S. and India.
- Hire, mentor, and develop senior data engineers, knowledge-graph engineers, machine-learning engineers, and data scientists while establishing a high technical bar across the organization.
- Operate a paired-team model with the India engineering organization, establishing standards and roadmaps across the broader team while enabling strong build and operational ownership in India.
- Lead the transition of data and intelligence architecture capabilities from expert partners to Baker Tilly through a structured shadow, pair, lead, and own model.
- Translate partner-developed architecture into Baker Tilly-owned roadmaps, standards, patterns, runbooks, and internal expertise.
- Partner with the Principal Product Manager for Compass / the Intelligence Platform on product roadmap, priorities, scope, and data requirements.
- Partner with the Chief Architect to co-design the semantic architecture and ensure alignment across the intelligence, agentic, and platform layers.
- Collaborate with the Head of Agent Engineering to ensure Compass workflows and agentic experiences are grounded in high-quality data and retrieval capabilities.
- Partner with Platform & Production Engineering on the infrastructure supporting the Intelligence Layer, including vector and retrieval infrastructure, runtime environments, and production telemetry.
- Collaborate with subject matter experts to ensure the Intelligence Layer accurately captures and represents practitioner knowledge and judgment.
- 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 experience across data architecture, data engineering, and data platforms, including technical leadership of senior engineering teams.
- Demonstrated experience designing and delivering knowledge systems embedded within products, including ontologies, knowledge graphs, semantic layers, or similar capabilities that directly support applications.
- Deep expertise in knowledge representation, including ontologies, knowledge graphs, GraphRAG, and judgment or decision modeling.
- Strong experience with data architecture and modeling, including enterprise and product data models, identity resolution, and data contracts.
- Strong modern lakehouse experience using Databricks or equivalent technologies, including Delta Lake, medallion architecture, and structured and unstructured data pipelines at scale.
- Experience designing and operating streaming and batch data pipelines and unstructured-document processing capabilities.
- Deep understanding of retrieval architectures, including embeddings, vector search, hybrid graph and vector retrieval, and reranking approaches.
- Experience building retrieval systems in which quality is measured, evaluated, and continuously improved.
- Experience leading or closely partnering with data science and machine-learning teams to establish production learning loops.
- Knowledge of applied data science practices for feedback capture, evaluation-driven improvement, and drift detection and management.
- Experience with corpus engineering, including document extraction, ground-truth development, and synthetic data generation.
- Demonstrated ability to design scalable data and intelligence architectures while remaining hands-on with complex technical challenges.
- Experience leading distributed engineering teams and collaborating effectively across U.S. and India-based organizations.
- Experience transitioning architecture, engineering capabilities, or technical ownership from external partners to internal teams.
- Strong ability to collaborate across Product, Architecture, Engineering, Data, and business subject matter experts to translate product and business needs into scalable technical solutions.
- Demonstrated ability to use AI and agentic development tools as a force multiplier in day-to-day technical and leadership work.
- Ability to operate effectively as a player-coach, balancing hands-on architecture and technical problem-solving with team leadership, talent development, and strategic direction.
- 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 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. 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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