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About the Business
LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti\-Money Laundering/Counter Terrorist Financing, Identity Authentication \& Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at https://risk.lexisnexis.com/
About the Role
We are seeking a visionary, results\-oriented AI Strategy \& Execution Leader to accelerate the adoption and business impact of Artificial Intelligence across the Product Delivery and Consulting and broader Global Consulting and Operations organization. This leader will be responsible for defining and driving our AI strategy, managing a portfolio of AI initiatives, and ensuring successful execution, value realization, and disciplined tracking of measurable results across business functions.
The ideal candidate is both strategic and operational. They can identify transformative opportunities, align stakeholders around a common vision, and drive execution through complex, matrixed environments. They possess the ability to connect business objectives with emerging AI capabilities, transforming innovative ideas into practical, scalable solutions that deliver measurable outcomes.
In addition to overseeing AI strategy and delivery, this leader will champion enterprise AI adoption through training, communications, change management, and employee engagement programs. They will lead our GCO Team Effectiveness Challenge, creating a culture of innovation while establishing mechanisms to capture, evaluate, prioritize, and scale AI\-driven opportunities across the organization.
This role requires exceptional collaboration, communication, and influence skills, as well as a passion for continuous improvement, operational excellence, and business transformation through AI.
Preferred candidate is local to the following offices for occasional in\-person meetings: Alpharetta, GA, Dayton, OH, Boca Raton, FL
Responsibilities
- Define and maintain the AI strategy, roadmap, and enterprise priorities.
- Lead and govern the PDC AI portfolio from idea intake through execution, adoption, and scale.
- Drive team effectiveness, AI adoption, training, communications, and champion engagement across the organization.
- Influence cross\-functional leaders and stakeholders to align priorities, remove barriers, and deliver outcomes.
- Track measurable results, adoption, productivity gains, efficiency improvements, and ROI.
- Create executive\-ready communications, thought leadership, and progress narratives that demonstrate value.
Experience Level: Senior Leader with a passion for driving change and progress
Requirements
- Bachelor's degree in Business, Technology, Data Science, Engineering, Organizational Leadership, or a related field.
- 8\+ years of experience in strategy, transformation, program leadership, management consulting, product management, organizational effectiveness, or related disciplines.
- 2\+ years of experience leading enterprise\-wide initiatives involving AI, automation, advanced analytics, digital transformation, or emerging technologies.
- Demonstrated success driving large\-scale, cross\-functional programs within highly matrixed organizations.
- Proven ability to influence stakeholders and achieve outcomes without direct authority.
- Experience developing enterprise strategies, roadmaps, governance models, and execution frameworks.
- Strong program and portfolio management experience, including prioritization, resource management, risk management, and executive reporting.
- Exceptional written, verbal, and presentation communication skills.
- Experience developing executive communications, strategic documentation, and business narratives.
- Strong analytical and problem\-solving abilities with experience measuring business outcomes and ROI.
- Experience leading organizational change management and adoption initiatives.
Preferred Qualifications
- Experience building and leading enterprise AI adoption programs or Centers of Excellence.
- Experience managing innovation programs, team effectiveness initiatives, hackathons, idea incubators, or continuous improvement programs.
- Experience developing and leading AI Champion or Ambassador networks.
- Experience designing and delivering enterprise learning, training, and enablement programs.
- Deep understanding of Generative AI, AI agents, machine learning, automation platforms, and responsible AI practices.
- Experience partnering with communications, learning, technology, and business teams to drive enterprise\-wide adoption.
- Management consulting or transformation consulting experience preferred.
- MBA or advanced degree in Business, Technology, Data Science, Organizational Leadership, or a related field.
U.S. National Base Pay Range: $118,300 \- $219,800\. Geographic differentials may apply in some locations to better reflect local market rates.This job is eligible for an annual incentive bonus.
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Salary Context
This $118K-$219K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LexisNexis Risk Solutions, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($169K) sits 21% below the category median. Disclosed range: $118K to $219K.
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.
LexisNexis Risk Solutions AI Hiring
LexisNexis Risk Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alpharetta, GA, US. Compensation range: $219K - $219K.
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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