Interested in this AI/ML Engineer role at LexisNexis Legal & Professional?
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About This Role
\*\*\* PLEASE NOTE: This role is on\-site/hybrid in Raleigh, NC \*\*\*
Are you passionate about building next\-generation AI solutions that transform how professionals work with information and insights?
Do you thrive on solving complex challenges with Retrieval\-Augmented Generation (RAG), agentic AI systems, and cloud\-native machine learning technologies?
About our Team
LexisNexis Legal \& Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX ( http://www.relx.com ), a global provider of information\-based analytics and decision tools for professional and business customers. Our company has been a long\-time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi\-model approach that prioritizes using the best model from today’s top model creators for each individual legal use case. The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles ( https://stories.relx.com/responsible\-ai\-principles/index.html ).
About the Role
We are seeking a highly skilled AI Engineer with expertise in Retrieval\-Augmented Generation (RAG) and strong Python programming skills to join our innovative team. The ideal candidate will have a deep understanding of AI and machine learning principles, experience with RAG models, and a passion for developing cutting\-edge AI solutions.
Key Responsibilities:
- Test, evaluate and and implement AI models with a focus on Retrieval\-Augmented Generation (RAG).
- Strong prompt engineering and prompt testing skills to derive the desired outcome
- Collaborate with cross\-functional teams to integrate AI solutions into existing systems.
- Optimize and fine\-tune RAG models for performance and scalability cost saving.
- Conduct research and stay up\-to\-date with the latest advancements in AI and machine learning.
- Design, build, and deploy agentic AI systems capable of autonomous planning, tool use, and multi\-step task execution using frameworks such as LangGraph, AutoGen, or CrewAI.
- Architect and deploy AI/ML and RAG solutions on AWS, leveraging services such as SageMaker, Bedrock, Lambda, and S3\.
- Write clean, efficient, and maintainable code in Python.
- Perform data preprocessing, feature engineering, and model evaluation.
- Troubleshoot and debug AI models and applications in a mono\-repo settings.
- Document AI models, processes, and workflows.
Qualifications:
- Proven experience as an AI Engineer or similar role.
- Strong proficiency in Python programming.
- In\-depth knowledge of Retrieval\-Augmented Generation (RAG) models.
- Proven experience in prompt engineering
- Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch).
- Proficiency in data preprocessing and feature engineering techniques.
- Hands\-on experience building agentic AI applications, including tool/function calling, multi\-agent orchestration, and autonomous workflow design.
- Demonstrated experience with AWS cloud services (e.g., SageMaker, Bedrock, Lambda, EC2, S3\) for developing and deploying AI/ML solutions.
- Excellent problem\-solving skills and attention to detail.
- Strong communication and teamwork abilities.
Preferred Skills:
- Experience with cloud platforms, particularly AWS (e.g., SageMaker, Bedrock, Lambda, ECS/EKS, S3\); familiarity with Azure or Google Cloud is a plus.
- Experience with agentic AI frameworks and tooling (e.g., LangGraph, LangChain, AutoGen, CrewAI, AWS Bedrock Agents, Model Context Protocol).
- AWS certification (e.g., AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect).
- Knowledge of natural language processing (NLP) techniques.
- Familiarity with version control systems (e.g., Git).
- Experience with deploying AI models in production environments.
Work in a Way That Works for You
We promote a healthy work/life balance across the organisation. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long\-term goals.
Working Pattern
Working flexible hours \- flexing the times when you work in the day to help you fit everything in and work when you are the most productive.
About the Business
LexisNexis Legal \& Professional® provides legal, regulatory, and business information and analytics that help customers increase their productivity, improve decision\-making, achieve better outcomes, and advance the rule of law around the world. As a digital pioneer, the company was the first to bring legal and business information online with its Lexis® and Nexis® services. \#AIFluent
U.S. National Base Pay Range: $104,900 \- $174,700\. Geographic differentials may apply in some locations to better reflect local market rates.This job is eligible for an annual incentive bonus.
We know your well\-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1\-855\-833\-5120\.
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We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.
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Salary Context
This $104K-$174K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LexisNexis Legal & Professional, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($139K) sits 36% below the category median. Disclosed range: $104K to $174K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
LexisNexis Legal & Professional AI Hiring
LexisNexis Legal & Professional has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Raleigh, NC, US, IL, US. Compensation range: $174K - $192K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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