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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.
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:
The Managing Director, AI Platform Architect is the founding architect of Baker Tilly's AI Product Group (APG) — the senior-most individual contributor in the organization and the single design authority for the product, agentic, and data architecture of everything APG builds. The architect designs the agent platform the firm's AI products run on (agent harness, orchestration, tool and MCP integration, retrieval, memory and context management, and the evaluation infrastructure that decides what ships), co-designs the intelligence substrate (ontology, knowledge graph, judgment layer) with the substrate lead, chairs APG's technical design authority, and serves as the CAITO's senior technical counsel.
This is a hands-on, build-first role: the architect prototypes the patterns product teams then harden, sets the technical bar for the founding engineers, and leads the deliberate absorption of architecture from the firm's expert engineering partners so Baker Tilly owns its platform rather than renting it.
Responsibilities
- Architecture design and standards. + Own the target-state architecture for the AI product portfolio and the architecture decision records that make significant decisions transparent, durable, and teachable. + Design the agent platform: harness and orchestration patterns, tool and MCP integration, retrieval architecture spanning vector and graph, memory and context management, and the shared services every product consumes. + Co-design the intelligence substrate (ontology, knowledge graph, judgment layer) with the substrate lead. + Set and steward a model-agnostic model strategy — selection, routing, fallback, fine-tuning versus context strategies, and cost-per-outcome economics — and keep the platform portable and vendor-neutral.
- Hands-on technical leadership. + Design by building: prototype the patterns product teams then harden. + Review and shape each product's architecture with the engineering teams — hands-on, in the code. + Chair APG's technical design authority. + Lead architecture absorption from the firm's expert engineering partners on a shadow-pair-lead-own ramp, converting partner-built patterns into firm-owned standards, decision records, and runbooks, with defined gates at which each architecture layer becomes firm-owned.
- Evaluation standards and production readiness. + Establish evaluation-first engineering as an architectural standard: no capability is production-ready until its evaluation suite says so. + Define the non-functional architecture (scalability, deployment, security, observability) with enterprise technology partners so products transition cleanly into enterprise operation. + Co-own the production handoff standard and exercise it on the first products through the gate. + Operate the architectural seam with Enterprise Architecture — full design authority inside APG products, built to enterprise standards, data contracts, and production gates.
- Technical counsel and talent. + Advise the CAITO on build, buy, and integrate decisions across the portfolio, including vendor AI architecture boundaries where the firm must own the data and knowledge layer. + Help hire and mentor the founding engineers; raise the bar on design quality, code quality, and written technical decision-making. + Represent APG architecture in the firm's architecture forums and, with the CAITO, to executive leadership.
- Engage and participate in special projects or complete other duties as assigned.
Qualifications
- Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience; advanced degree a plus.
- No certifications or licenses required.
- 15+ years in software and platform architecture or engineering, including senior engineering leadership (director level or above) or principal/consulting-architect scope across multiple teams.
- Recent, hands-on depth in LLM-based and agentic systems carried to production — including agentic infrastructure itself: control planes, agent runtimes, gateways, SDKs, and the developer experience around them. Founder or zero-to-one platform experience is highly valued.
- Has personally designed and shipped an AI platform, agent framework, or ML platform at meaningful scale — and can walk through the decisions, trade-offs, and failures.
- Experience operating as a principal or consulting architect across multiple teams — setting direction through standards, prototypes, and persuasion rather than authority alone.
- Experience partnering with enterprise architecture, security, and governance functions in a large organization without losing product velocity.
- Deep fluency in the modern agentic stack: orchestration frameworks, tool/function calling and MCP, retrieval (vector and graph), context and memory management, and evaluation methodology.
- Strong data architecture background: lakehouse platforms, semantic and graph modeling, data contracts, and pipeline architecture for structured and unstructured data.
- Knowledge representation: ontologies, knowledge graphs, GraphRAG, judgment/decision modeling.
- LLM platform engineering: gateways, routing, evaluation harnesses, observability, token economics; model strategy across frontier and open models.
- Enterprise integration: APIs, event-driven patterns, data contracts, identity and entitlements.
- Writes production-quality code and prototypes; recent hands-on work in Python and/or TypeScript.
- Demonstrates personal AI leverage: designs and prototypes with agentic tools as a force multiplier — the working standard for this team.
- Builder-first temperament with strong architectural judgment; pragmatic about which decisions must be made once and well versus delegated; raises the technical bar of everyone in range; writes things down; makes complex systems explainable to executives and practitioners; treats the firm's platform, substrate, and runbooks as owned assets.
The pay rate range for the position is $350,000-$550,000. 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.
All qualified internal candidates will be considered for this position, irrespective of their current geographic location.
Salary Context
This $350K-$550K 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 ($450K) sits 112% above the category median. Disclosed range: $350K to $550K.
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
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 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
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