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About This Role
Job Description
HR AI Transformation Specialist
S4
Summary/objective
Reporting to the VP of HR Operations, the HR AI Transformation Specialist supports the delivery of key transformation initiatives across the HR function. As a highly skilled specialist, this individual contributor leads end\-to\-end initiative delivery, driving change management, process design, and the implementation of strategic programs that modernize and enhance HR capabilities. The role also helps establish repeatable standards, practical methodologies, and adoption approaches, including the responsible use of AI tools such as Claude, that make HR more consistent, connected, and scalable over time. isolved operates as an Anthropic shop with Claude as its primary AI platform, so this role applies AI in a real enterprise environment with tools already in place.
Core Job Duties
- Lead and execute HR transformation initiatives from planning and design through implementation, delivering against defined objectives, timelines, and success metrics.
- Partner with HR functional leads, HRIS, Finance, Legal, and business stakeholders to translate priorities into actionable plans and integrated deliverables.
- Assess upstream and downstream impacts across HR processes, systems, data, and roles so initiatives are designed as part of an integrated environment rather than in isolation.
- Apply structured change management approaches to drive adoption of new processes, tools, and ways of working across the organization.
- Develop and manage detailed project plans, tracking risks, dependencies, and milestones, and communicate progress clearly to leadership.
- Analyze, redesign, and document end\-to\-end HR processes, identifying pain points, handoffs, and system impacts to improve efficiency, scalability, and employee experience.
- Identify opportunities for standardization and continuous improvement to reduce fragmentation across HR teams.
- Partner with HRIS and technology teams to align process design with system capabilities and practical AI\-enabled improvements, including Claude\-powered workflows where applicable.
- Contribute to transformation methodologies, tools, templates, and governance standards, and support governance frameworks that create visibility across initiatives, dependencies, and cross\-functional impacts.
- Support the responsible and consistent use of AI within HR, including Claude, in alignment with enterprise policies, data privacy requirements, and risk considerations.
- Develop stakeholder education, training, and job aids, and build organizational capability in change adoption, process excellence, and practical AI use, including effective use of Claude across HR workflows.
Job Complexity
As a highly skilled specialist, contributes to the development of concepts and techniques. Completes complex tasks in creative and effective ways.
Interaction
Consistently works on complex assignments requiring independent action and a high degree of initiative to resolve issues. Makes recommendations for new procedures.
Supervision
Acts independently to determine methods and procedures on new assignments. Exercises initiative and judgement in completing recurring assignments. Often acts as a facilitator and team leader.
Experience
Typically requires a Bachelor's degree or equivalent work experience; and 5–8 years of related experience in HR transformation, HR operations, consulting, or program delivery.
Scope
N/A
Discretion
N/A
Minimum Qualifications
- Bachelor's degree required; advanced degree (MBA or related field) preferred but not required.
- 5–8 years of related experience in HR transformation, HR operations, consulting, or program delivery, with demonstrated ownership of initiatives from strategy through execution.
- Proven experience leading change management, process design, and transformation delivery in dynamic, cross\-functional environments.
- Track record delivering integrated initiatives involving stakeholder alignment, dependency management, and measurable outcomes.
- Experience working within or closely alongside HR functions, including HR Operations, Talent Management, or HR Technology.
- Strong change management and stakeholder influence skills, with the ability to drive adoption across diverse audiences without formal authority.
- A structured problem\-solver and systems thinker who connects details to the broader operating model and anticipates impacts across process, technology, data, roles, and stakeholders.
- Excellent written and verbal communication skills, including the ability to prepare executive\-level materials and translate complexity into practical guidance.
- Ability to manage multiple initiatives simultaneously with strong follow\-through and a bias toward closure.
- Familiarity with AI tools, including generative AI, workflow automation, or digital assistants, and their practical application in HR or business operations contexts.
- Direct experience with Claude or Anthropic's platform preferred but not required.
Physical Demands
Prolonged periods of sitting at a desk and working on a computer. Must be able to lift up to 15 pounds.
Travel Required
Yes, up to 15% domestic travel may be required.
Work Authorization
Employee must be legally authorized to work in the United States.
FLSA Classification
Exempt
Location
Office/Hybrid/Remote
Internal Job Title
HR AI Transformation Specialist
Effective Date
7/28/2026
About isolved
isolved is an employee experience leader, providing intuitive, people\-first HCM (Human Capital Management) technology. Our solutions are delivered directly or through our partner network to more than five million employees and 145,000 employers \- who use them every day to boost performance, increase productivity, and accelerate results while reducing risk. Our HCM platform, isolved People Cloud, seamlessly connects and manages the employee journey across talent management, HRpayroll, workforce management and engagement management functions. No matter the industry, we help high\-growth organizations employ, enable and empower their workforce by transforming employee experience for a better today and a better tomorrow. For more information, visit www.isolvedhcm.com.
isolved is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. isolved is a progressive and open\-minded meritocracy. If you are smart and good at what you do, come as you are. Visit www.isolvedhcm.com/careers for more information regarding our incredible culture and focus on our employee experience. Visit www.isolvedeebenefits.com for a comprehensive list of our employee total rewards offerings.
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 isolved, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
isolved AI Hiring
isolved has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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