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About This Role
Overview
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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:
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The Senior Associate Product Manager supports the strategy, delivery, adoption, and responsible operation of the organization’s enterprise large language model (LLM) portfolio, with primary focus on ChatGPT Enterprise and Claude Enterprise; including chat, api, and coding related products. This hands\-on role translates user and stakeholder needs into product priorities, coordinates execution across U.S.\- and India\-based teams, and improves access, support, enablement, governance, and cost efficiency.
Essential Job Functions \& Duties:
- (25%) Platform Administration and Operational Excellence: Lead hands\-on administration of enterprise AI platforms, including workspace configuration, access provisioning, license and permission management, SSO/RBAC coordination, user lifecycle processes, API access intake, governance checks, and operational documentation. Identify and implement opportunities to automate, standardize, and streamline recurring workflows.
- (20%) Product Operations and Global Delivery Management: Provide product direction, priorities, requirements, user stories, and success criteria to India\-based operations and execution teams. Coordinate sprint planning, backlog refinement, cross\-time\-zone handoffs, asynchronous updates, delivery reviews, dependency management, structured feedback, and blocker resolution.
- (20%) Product Strategy, Roadmap, and Platform Evolution: Support product strategy, multi\-quarter roadmaps, operating models, and prioritized backlogs for ChatGPT Enterprise, Claude Enterprise, approved LLM APIs, Codex, Claude Code, and related tools. Evaluate new features, models, and vendor releases for enterprise fit, risk, usability, and business value; coordinate pilots, release planning, configuration changes, product enhancements, communications, and rollout activities.
- (15%) Governance, Risk, and Responsible Use: Maintain usage policies, acceptable\-use guidance, access standards, governance documentation, and operating procedures in partnership with Security, Compliance, Legal, Finance, and AI governance stakeholders. Identify and escalate potential misuse, control gaps, and emerging risks while balancing appropriate safeguards with usability and business enablement.
- (10%) User Support, Adoption, and Enablement: Own the end\-user experience across onboarding, access, configuration, adoption, and support. Serve as a U.S.\-hours escalation point, diagnose product and workflow issues, coordinate technical resolution, and improve intake, routing, ownership, escalation, and service\-level expectations. Partner with Learning \& Enablement, Innovation, and Citizen Development to develop training materials, product guides, FAQs, office hours, prompt examples, and responsible\-use resources.
- (10%) Developer Enablement, Measurement, and Stakeholder Communication: Support approved LLM APIs and coding tools by defining access models, onboarding processes, usage standards, cost and rate\-limit considerations, developer guides, starter templates, prompt libraries, and reference patterns. Monitor adoption, active usage, onboarding completion, satisfaction, support quality, license utilization, platform performance, and costs; recommend product, budget, license, and process improvements. Communicate product status, decisions, risks, dependencies, and outcomes to technical teams, business stakeholders, and leadership.
- Perform other duties and contribute to related initiatives as assigned.
Minimum Qualifications:
- Bachelor’s degree in computer science, business, information systems, or a related field required
- 3 years of experience in product management, technical product ownership, platform operations, or a related field, including experience supporting enterprise SaaS, AI, API, developer\-platform, or technical\-enablement products.
- Working knowledge of enterprise LLM products and APIs, including ChatGPT Enterprise, Claude Enterprise, Codex, Claude Code, or comparable platforms; product lifecycle and backlog practices; access, provisioning, and support workflows; prompt and model concepts; adoption and enablement methods; usage governance, cost controls, and responsible AI principles.
- Familiarity with identity management, SSO, RBAC, API security, usage controls, or rate limits is preferred.
- Strong product judgment, requirements definition, roadmap and backlog management, stakeholder alignment, analytical problem\-solving, documentation, facilitation, and written and verbal communication skills.
- Able to prepare product briefs, user guides, release communications, requirements, and leadership updates and to use tools such as Azure DevOps, Confluence, and Microsoft 365\.
- Able to balance strategic product thinking with hands\-on operational follow\-through; translate user feedback into prioritized improvements; coordinate distributed teams across time zones; manage multiple priorities; and work effectively with technical, risk, finance, enablement, and business partners.
- English proficiency required.
*The pay rate range for this job position are listed below. 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.*
*The national pay rate range is $66,100 to $114,020*
\#LI\-AC1
\#LI\-Remote
Salary Context
This $66K-$114K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($90K) sits 58% below the category median. Disclosed range: $66K to $114K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Baker Tilly Canada AI Hiring
Baker Tilly Canada has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $114K - $114K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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