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About This Role
Grant Thornton US is building a market\-leading AI practice focused on practical, secure, and scalable outcomes for our clients, and we are hiring AI/ML Solution Architects to lead discovery phase through production delivery. Hiring in most major US cities.
As an AI\-ML Architect you will translate business objectives into end\-to\-end AI architectures across ML/AI, application integration, data and governance—defining target\-state designs, reference patterns, and implementation roadmaps; guiding technology choices across cloud and modern stacks; and partnering with security, risk, and delivery leaders to ensure solutions are operable, compliant, and measurable. The ideal candidate brings the right blend of hands\-on AI engineering credibility and consulting leadership—strong communication with executives and technical teams, experience designing AI solutions that integrate with enterprise systems, and a pragmatic approach to tradeoffs across accuracy, cost, latency, reliability, and risk—along with the ability to mentor teams and shape repeatable assets and accelerators. We are actively recruiting for: Manager level (5\+ years) for architects, and Director level (7\+ years) for senior architects who can set technical standards, drive quality and reliability, and help shape reusable patterns and accelerators. This role is specifically a strategic investment to grow our AI capabilities and advisory services.
If you want your work to matter, this is the moment: we are not building “another AI consulting practice”—we are rewriting the playbook for how clients deliver on the promise of AI. We’re building a practice where teams love the pace, the craft, and the real\-world impact.
Day\-to\-day responsibilities:
- Lead discovery workshops to clarify business objectives, constraints, and measurable success criteria for AI/ML initiatives
- Translate requirements into end\-to\-end target\-state architectures across data, ML/AI, application integration, security, and governance
- Define pragmatic tradeoffs across accuracy, latency, cost, reliability, privacy, and risk—and communicate decisions to exec and engineering audiences
- Design the data \+ model lifecycle (pipelines, training/finetuning, serving, monitoring, drift detection, retraining) and the required MLOps/LLMOps foundations
- Establish integration patterns with enterprise systems (APIs/events/workflows, IAM, observability) so solutions are operable and supportable in production
- Partner with security, privacy, and risk teams to embed controls (access, auditability, data handling, responsible AI) into solution designs
- Produce core delivery artifacts (architecture diagrams, reference patterns, implementation roadmap, runbooks) and drive architecture reviews
- Mentor teams and build reusable assets/accelerators (reference architectures, templates, evaluation scorecards) to scale repeatable delivery quality
You have the following technical skills and qualifications:
- Bachelor's degree preferably in data science or computer science or related discipline
- For managers, minimum five years of hands\-on developer experience in machine learning and artificial intelligence stacks
- For Directors, at least two years of experience leading teams of AI/ML architects and developers
- Demonstrated experience designing and delivering production AI/ML solutions in an enterprise environment
- Strong grounding in cloud architecture (AWS/Azure/GCP), distributed systems, and modern data platforms
- Experience with MLOps practices (model lifecycle, monitoring, governance, deployment automation)
- Experience partnering with security/risk to implement privacy, access controls, auditability, and responsible AI practices
- Ability to lead senior client stakeholders through decisions under ambiguity
- Experience to develop long\-standing relationships with clients
- Experience leading AI/ML delivery programs
- Experience with AI patterns (RAG, agentic, etc.)
- Preferred: experience with regulated environments (SOX, HIPAA, PCI, model risk management)
- Experience leading proposal solutioning / estimates / technical writing for pursuits
- Experience mentoring junior or senior colleagues in AI/ML architectures
- Experience guiding clients on build vs. buy decisions of AI/ML powered use cases
- Flexible, adaptable and an eager self\-starter
- English: Fluent spoken and written communications skills
- Prior consulting industry experience or prior experience in an internal consulting role
- Consistent with the firm’s hybrid work model, this position will require in\-person attendance at least two days a week either at a Grant Thornton office or at a client site
- Readiness to travel up to 60%
- Must be currently eligible to work in the United States, position is not eligible for employer sponsorship
- Consistent with the firm’s hybrid work model, this position will require in\-person attendance at least two days per week, either at a Grant Thornton office or client site
\#Hybrid
\#LI\-LG1
*The base salary range for this position is between $170,568 and $274,554\. Placement within the pay range is at Grant Thornton’s discretion, and it is based on multiple factors, including but not limited to, job \-related knowledge/skills, experience, business needs, progression within the role, geographic location, and internal equity. At Grant Thornton, compensation decisions are dependent upon the facts and circumstances of each position and candidate.*
Salary Context
This $170K-$274K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Grant Thornton, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $170K to $274K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Grant Thornton AI Hiring
Grant Thornton has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Philadelphia, PA, US, Fort Lauderdale, FL, US. Compensation range: $274K - $300K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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