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About This Role
The AI Adoption & Change Management Consultant sits within the AI Center of Excellence and works with business leaders, functional teams, IT, HR, Communications, learning partners, and AI initiative owners to accelerate responsible adoption of AI capabilities across the enterprise. This role develops and executes AI-focused change management strategies that build awareness, readiness, confidence, and sustained usage of approved AI tools and solutions while supporting the Director of AI Adoption and Value Realization in measuring adoption, engagement, business value, and realized impact from AI investments.
Job duties include contact with other employees and access confidential and proprietary information and/or other items of value, and such access may be supervised or unsupervised. The Company therefore has determined that a review of criminal history is necessary to protect the business and its operations and reputation and is necessary to protect the safety of the Company’s staff, employees, and business relationships.
Position is not remote/hybrid and will report to our Cleveland Global Headquarters.
At Sherwin-Williams, our purpose is to inspire and improve the world by coloring and protecting what matters. Our paints, coatings and innovative solutions make the places and spaces in our world brighter and stronger. Your skills, talent and passion make it possible to live this purpose, and for customers and our business to achieve great results. Sherwin-Williams is a place that takes its stability, growth and momentum and translates it to possibility for our people. Our people are behind the strength of our success, and we invest and support you in:
Life … with rewards, benefits and the flexibility to enhance your health and well-being
Career … with opportunities to learn, develop new skills and grow your contribution
Connection … with an inclusive team and commitment to our own and broader communities
It's all here for you... let's Create Your Possible
At Sherwin-Williams, part of our mission is to help our employees and their families live healthier, save smarter and feel better. This starts with a wide range of world-class benefits designed for you. From retirement to health care, from total well-being to your daily commute—it matters to us. A general description of benefits offered can be found at http://www.myswbenefits.com/. Click on “Candidates” to view benefit offerings that you may be eligible for if you are hired as a Sherwin-Williams employee.
Compensation decisions are dependent on the facts and circumstances of each case and will impact where actual compensation may fall within the stated wage range. The wage range listed for this role takes into account the wide range of factors considered in making compensation decisions including skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. The wage range, other compensation, and benefits information listed is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law.
Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable federal, state, and local laws including with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act where applicable.
Sherwin-Williams is proud to be an Equal Employment Opportunity employer. All qualified candidates will receive consideration for employment and will not be discriminated against based on race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, pregnancy, genetic information, creed, marital status or any other consideration prohibited by law or by contract.
As a VEVRAA Federal Contractor, Sherwin-Williams requests state and local employment services delivery systems to provide priority referral of Protected Veterans.
Please be aware, Sherwin-Williams recruiting team members will never request a candidate to provide a payment, ask for financial information, or sensitive personal information like national identification numbers, date of birth, or bank account numbers during the application process.
- Manage a portfolio of AI change initiatives, including enterprise AI platforms, priority AI use cases, AI-enabled ways of working, and adoption programs requiring structured change management support.
- Create and execute AI change management plans tailored to audience, risk, readiness, and business impact, including stakeholder engagement, communications, training, reinforcement, adoption measurement, and value realization support.
- Partner with the Director of AI Adoption and Value Realization to define adoption and value measurement approaches for AI initiatives, including success metrics, baselines, targets, dashboards, feedback loops, and executive reporting inputs.
- Assess AI change readiness across stakeholder groups and translate insights into targeted interventions that improve awareness, confidence, responsible usage, behavior change, and sustained adoption.
- Support AI literacy, enablement, and self-service resources that help employees and leaders understand approved AI tools, responsible use expectations, practical use cases, and role-specific adoption opportunities.
- Collect, analyze, and publish adoption and change effectiveness metrics, including awareness, training completion, active usage, use case engagement, sentiment, barriers, behavior change, and value realization indicators.
- Provide data-driven insights and recommendations to AI initiative teams and business leaders to improve adoption, remove friction, prioritize enablement needs, and increase measurable value from AI solutions.
- Coordinate with AI champions, change networks, Communications, HR, Learning, IT, Legal, Privacy, and Security partners to reinforce responsible AI adoption and share leading practices across the enterprise.
Required:
- Bachelor’s degree or foreign equivalent in Business, Communications, Strategy, Organizational Development, Human Resources, Information Systems, Data/Analytics, or related field ii. Change Management certification or demonstrated equivalent experience supporting technology-enabled change
- 6+ years post-college experience.
- 5+ years’ experience delivering organizational change management for enterprise technology, digital transformation, AI, data, automation, or business process initiatives.
- Academic and applied understanding of change management fundamentals, with ability to adapt methods for AI adoption, responsible use, employee readiness, and behavior change.
- Strong verbal and written communication skills, including ability to translate AI concepts, adoption expectations, and value messages for executive, manager, and employee audiences.
- Strong PC skills including Microsoft Word, Excel, Teams, SharePoint, PowerPoint, and experience using dashboards, survey tools, collaboration platforms, or AI-enabled productivity tools.
- Ability to effectively collaborate with executive leaders, AI COE teammates, and cross-functional partners with poise, tactfulness, and diplomacy.
- Demonstrated ability to build relationships with stakeholders across multiple business units, regions, and functions to advance AI adoption and value realization.
- Demonstrated ability to manage competing priorities, interpret adoption data, identify barriers, and pivot change plans based on business needs and stakeholder feedback.
- Experience supporting adoption and value measurement, including defining success metrics, collecting feedback, analyzing usage or engagement trends, and preparing insights for leadership review.
- Proficient with Microsoft Office Suite, Teams, SharePoint, and collaboration tools used to deliver communications, training, stakeholder engagement, and adoption reporting.
- Capable of multi-tasking, highly organized, and able to meet deadlines consistently across multiple AI change initiatives.
- Ability to work as part of the AI COE in a team-oriented, cross-functional environment with AI product, technology, business, communications, HR, learning, and governance partners.
- Ability to develop positive working relationships and build trust with leaders, AI champions, managers, and employee stakeholder groups.
- Strong analytical skills with ability to interpret adoption, sentiment, readiness, engagement, and value realization data and translate insights into actionable recommendations.
- Able to work in a complex and fast-paced environment where AI capabilities, use cases, stakeholder needs, and responsible use expectations continue to evolve.
Preferred:
- Experience working within an AI Center of Excellence, digital transformation office, analytics team, automation team, or enterprise technology adoption function.
- Experience leading change in a multi-region environment.
- Experience partnering with HR, Corporate Communications, Learning, IT, Data/Analytics, Legal, Privacy, Security, and business leaders to drive responsible AI adoption.
- Experience creating adoption dashboards, value realization scorecards, change health reports, or executive-ready measurement summaries.
- Experience with AI productivity tools, Copilot or similar enterprise AI platforms, adoption analytics, survey platforms, learning platforms, Power BI, or change measurement tools.
Salary Context
This $108K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Sherwin-Williams, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($124K) sits 41% below the category median. Disclosed range: $108K to $140K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Sherwin-Williams AI Hiring
Sherwin-Williams has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Cleveland, OH, US. Compensation range: $140K - $165K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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