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Company Description
Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.
Job Description
You will transform how the Operations team delivers value by eliminating manual processes, reducing implementation timelines, and advocating automation and AI at scale. You will design business process workflows, inventory business processes and documentation and implement the automation/AI strategy for Operations. You will report to Experian Automotive's Senior Vice President, Auto Operations. Minimal travel is required.
What Experian looks for:
- Proven ability to translate complex, cross\-functional programs into scalable, measurable outcomes.
- 3\+ years of experience in automation and AI technologies such as Robotic Process Automation (RPA), intelligent document processing, conversational AI, agentic AI, and generative AI applications.
- 3\+ years of experience streamlining and automating sales processes (opportunity management, client vetting, pricing, contracts, compliance, client onboarding) using CRM and other integrated tools.
- People leadership, with the ability to inspire, mentor, and align teams around innovation and execution.
- Experience improving operational efficiency, reducing manual toil, and enhancing the employee and patient experience through automation.
- Corporate execution focus — expertise in navigating matrixed organizations, building consensus, and driving measurable outcomes in a large enterprise environment.
- A talent for simplifying technical concepts and fostering understanding across both business and technical team members.
Responsibilities
- Strategy \& Leadership: Define and lead the automation and AI strategy for Operations. Establish a roadmap that reduces implementation times and improves efficiency. Serve as the senior leader for automation and AI within Operations, representing needs with enterprise technology and vendor partners.
- Execution \& Delivery: Build and manage automation and AI capabilities (RPA, chatbots, agentic AI) where relevant. Promote a prioritized pipeline of automation/AI use cases with measurable impact. Operationalize standards, best practices, and governance for automation and AI solutions.
- Collaboration \& Influence: Partner with Operations leadership to identify pain points. Influence enterprise and tool governance decisions to ensure Operations needs are met.
- Enablement \& Adoption: Oversee training, communication, and change management for AI/automation adoption. Champion user enablement and citizen development. Build awareness of automation successes and foster a culture of innovation.
- Measurement and Outcomes: Manage process orchestration inventory \& maps and measure process effectiveness to standardize approach and inform automation pipeline. Define and track goals, including implementation time reduction, manual toil eliminated, user adoption, and financial impact. Report progress and Return on investment to Operations and Auto leadership.
Qualifications
- Bachelor's degree in Computer Science, Engineering, Operations Management, or a related field required.
- Post\-Graduate education or formal training / certification in Artificial Intelligence, Machine Learning, or Data Science.
- Experience applying AI principles in real\-world enterprise environments, with measurable outcomes tied to operational efficiency and cost reduction.
Additional Experience
- Led enterprise\-wide automation programs that reduced operational costs and improved process efficiency.
- Delivered AI\-driven solutions (e.g., predictive analytics, intelligent chatbots) that enhanced customer and employee experience.
- Managed cross\-functional teams to implement automation pipelines.
- Established governance frameworks for AI/automation adoption ensuring compliance and scalability.
- Negotiated with technology vendors and partners to integrate advanced AI capabilities into core operations.
- Championed change management programs to drive cultural adoption of automation and AI.
- Designed and implemented KPIs to measure automation impact on productivity and Return on investment.
- Experience using RPA and AI tools to streamline workflows and eliminate manual tasks.
- Experience building automation centers of excellence and encouraging citizen development.
Additional Information
Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose driven culture is multi award\-winning; World's Best Workplaces™ 2025 (Fortune Global Top 25\), Great Place To Work™ in 26 countries to name a few. Check out Experian Life on social or explore our Careers Site to understand why. Experian is also proud to be an Equal Opportunity and Affirmative Action employer. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
Our compensation reflects the cost of labor across several U.S. geographic markets. The base pay range for this position is listed above. Within this range, individual pay is determined by work location and additional factors such as job\-related skills, experience, and education. You will be also eligible for a variable pay opportunity.
Experian is proud to be an Equal Opportunity Employer for all groups protected under applicable federal, state and local law, including protected veterans and individuals with disabilities. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
Benefits/Perks:
- Great compensation package and bonus plan
- Core benefits including full medical, dental, vision, and matching 401K
- Fully remote environment
- Flexible time off including volunteer time off, vacation, sick and 12\-paid holidays
\#LI\-Remote
Salary Context
This $176K-$316K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Experian, 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 in Demand for This Role
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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($246K) sits 38% above the category median. Disclosed range: $176K to $316K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Experian AI Hiring
Experian has 6 open AI roles right now. They're hiring across AI Agent Developer, AI Architect, AI/ML Engineer. Positions span Scottsdale, AZ, US, US, Costa Mesa, CA, US. Compensation range: $155K - $364K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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