Interested in this AI/ML Engineer role at George E. Booth?
Apply Now →Skills & Technologies
About This Role
The AI Solutions Engineer is a strategic technology, business transformation, and hands-on engineering leader responsible for identifying, developing, building, implementing, and driving adoption of artificial intelligence and automation solutions across the organization. This role serves as the bridge between business operations and emerging technologies, ensuring AI initiatives generate measurable business value, improve operational efficiency, enhance decision-making, and support organizational growth.
This position partners with executive leadership, division leaders, and operational stakeholders to identify opportunities for automation, optimization, advanced analytics, and AI-enabled decision support. This position combines strong business acumen, technical understanding, project leadership, and change management to successfully implement solutions across sales, distribution, fabrication, service, finance, and administrative functions. The successful candidate will operate as a high-level individual contributor, serving simultaneously as a strategic advisor to leadership and the primary technical builder who writes code, configures data pipelines, and deploys production-ready AI applications.
Essential Functions:
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
•Develop and execute the organization’s AI strategy aligned with business objectives, operational priorities, and growth initiatives.
•Partner with executive and division leadership to identify opportunities where AI, automation, and advanced analytics can improve efficiency, profitability, customer experience, and decision-making.
•Establish and maintain an enterprise AI project roadmap based on business impact, implementation complexity, organizational readiness, risk, and return on investment.
•Evaluate emerging AI technologies, platforms, vendors, and industry trends to identify opportunities for operational and competitive advantage.
•Serve as a trusted advisor to executive leadership regarding AI capabilities, limitations, risks, opportunities, and strategic investments.
•Directly design, code, develop, implement, and manage the full lifecycle of AI-driven business solutions and automation workflows.
•Rapidly architect and build functional internal Proofs of Concept (PoCs) to validate automation viability before scaling solutions across business units.
•Analyze business processes and operational challenges to determine appropriate AI, automation, analytics, technology, or process-improvement solutions.
•Translate business requirements and strategic objectives into practical, high-performing technology solutions with measurable business outcomes.
•Coordinate cross-functional project teams consisting of operational leaders, business analysts, developers, data specialists, IT resources, and external partners.
•Oversee and directly execute the technical integration of AI solutions with existing business systems, including ERP, CRM, inventory management, field service, financial, and operational platforms.
•Identify and implement opportunities to improve sales forecasting, quoting and proposal development, pricing and margin analysis, inventory management, procurement, supply chain, fabrication scheduling, quality assurance, service operations, maintenance programs, and administrative workflows.
•Identify opportunities to reduce manual effort, eliminate process bottlenecks, improve data accessibility, and enhance organizational decision-making.
•Establish measurable objectives and key performance indicators for AI initiatives and communicate operational and financial results to leadership.
•Ensure AI projects are delivered within established scope, schedule, budget, security, and performance expectations.
•Establish governance standards for the development, deployment, monitoring, and responsible use of AI technologies.
•Ensure AI initiatives comply with organizational data security standards, privacy requirements, regulatory obligations, and internal governance requirements.
•Partner with IT, cybersecurity, legal, and operational stakeholders to identify and mitigate technology, data, security, and compliance risks.
•Establish controls to monitor AI solution accuracy, reliability, security, performance, utilization, and business impact.
•Develop and execute organizational adoption and change-management strategies to support successful implementation of AI technologies.
•Develop education and training initiatives to improve organizational understanding and effective utilization of AI-enabled tools.
•Foster a culture of innovation, continuous improvement, responsible experimentation, and adoption of emerging technologies.
Supervisory Responsibility:
This position has limited supervisory responsibility that directly relates to projects and customer relations, not personnel. This role operates as a high-level Individual Contributor.
Work Environment:
While performing the duties of this job, the employee will mostly be in a standard office setting seated at a desk with a computer and phone workstation. Use of typical office equipment such as, but not limited to printer, scanner, fax machine, keyboard and smart devices.
Physical Demands:
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. While performing the duties of this job, the employee is regularly required to talk and hear. This position is active and requires standing, walking, bending and kneeling. The employee may also sit for long periods of time while driving or working at a computer workstation. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception and ability to adjust focus.
Position Type and Expected Hours of Work:
This is a full-time position. Days and hours of work are Monday through Friday, 8:00 a.m. to 5:00 p.m., 40 hours per week. Occasional evening and weekend work may be required as job duties demand.
Preferred Education and Experience:
•Relevant AI, technology, project management, or cloud-platform certifications.
•8+ years of progressive experience in technology leadership, product management, digital transformation, or business systems implementation.
•Minimum 3 years of experience leading AI, machine learning, advanced analytics, or automation initiatives.
•Proven track record of hands-on deployment, showing individual capability to write code and ship functional data/AI tools from concept through deployment.
•Experience supporting industrial, manufacturing, distribution, service, or operations-focused organizations preferred.
Required Education and Knowledge:
•Bachelor’s degree in computer science, Engineering, Information Technology, Business Administration, Data Analytics, or related field required.
•Hands-on, production-level engineering proficiency in Python or equivalent language standard to modern AI software development.
•Practical technical understanding of Generative AI, Large Language Models (LLMs), engineering frameworks (e.g., LangChain, LlamaIndex), vector databases, retrieval-augmented generation (RAG), and foundational model APIs.
•Knowledge of enterprise business systems including ERP, CRM, inventory management, field service, and operational technology platforms.
•Exceptional communication and stakeholder management skills.
•Ability to communicate effectively with executives, technical teams, and frontline operational personnel.
•Strong analytical and problem-solving abilities.
•Experience managing technology vendors, consultants, and third-party solution providers.
•Understanding of cybersecurity, data governance, and responsible AI principles.
•Ability to translate strategic objectives into actionable technology solutions and measurable business outcomes.
Other Duties:
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice.
Benefits:
Full benefits package includes major medical, dental and vision with paid-time off and matching 401(k).
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At George E. Booth, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
George E. Booth AI Hiring
George E. Booth has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Greenwood, IN, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.