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About This Role
Applied AI Engineer
About the role
LexisNexis Legal \& Professional is hiring an Applied AI Engineer to help shape the next generation of AI\-powered legal products and developer experiences.
As an Applied AI Engineer at LexisNexis, you will partner with internal teams and enterprise stakeholders to help build AI\-powered applications and workflows on top of LexisNexis AI platforms, legal content, and AI\-powered workflows and agent\-based capabilities. You will work directly with engineering, AI engineering, and data science teams to design and implement production AI applications, agent workflows, and scalable LLM\-powered experiences that support complex legal and professional workflows.
This role sits at the intersection of AI engineering, data scientist, developer enablement, and customer engagement. You will partner with Product, Engineering, Applied Science, and AI Platform teams to support implementation decisions, accelerate AI adoption, and help teams adopt reusable AI engineering patterns and implementation best practices.
This is a deeply hands\-on role focused on building, prototyping, and iterating on AI\-powered experiences. The ideal candidate combines strong software engineering fundamentals with practical experience deploying LLM applications, agent systems, and AI\-native workflows in production environments.
What you’ll do
Start with customers
- Spend real time with lawyers, legal operations teams, and our internal subject\-matter experts — in their offices, on their calls, watching their workflows. Develop a strong understanding of customer workflows and operational challenges through direct engagement.
- Translate ambiguous, half\-formed customer pain into crisp problem statements the team can build against.
- Collaborate closely with customers and internal stakeholders to prototype, validate, and refine AI\-powered workflows and user experiences based on customer feedback and observed user needs.
- Bring the customer voice back into our roadmaps, our model choices, and our trade\-offs.
Occasional travel to customer sites may be required to better understand workflows and gather product feedback.
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Build AI\-powered applications and workflows
- Contribute to AI\-powered applications and workflows for legal and professional use cases, including leveraging existing RAG pipelines, research assistants, and related AI capabilities developed by ML engineering teams.
- Implement and iterate on LLM application capabilities such as prompt engineering, multi\-step workflows, tool calling, and lightweight agent patterns in collaboration with machine learning engineering teams.
- Contribute to scalable orchestration layers for prompting, retrieval, and tool integration across AI services.
- Work with frameworks such as LangChain, LangGraph, LlamaIndex, MCP/A2A, OpenAI SDKs, Google ADK, and/or Anthropic/Claude APIs to prototype and productionize AI capabilities.
Participate in experimentation, testing, and performance optimization activities for LLM\-based applications in production environments.
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Contribute to AI Engineering Enablement
- Support adoption of AI engineering practices by helping software engineering teams incrementally integrate machine learning and generative AI capabilities into existing products and workflows, in collaboration with AI/ML engineering teams.
- Promote reusable AI/ML engineering standards, tooling, and best practices that reduce friction for teams adopting AI and machine learning technologies, while aligning with recommendations from data science and AI platform teams.
- Help software engineers expand their capabilities in ML\-oriented development for applicable use cases without requiring deep data science specialization.
- Support teams in adopting AI\-assisted development workflows through prototyping, architecture collaboration, and hands\-on engineering support.
- Contribute to engineering for LLM applications, AI workflows, and AI\-enabled product development.
- Assist in building evaluation, monitoring, and observability tooling to improve AI application quality, reliability, and developer visibility.
- Collaborate with Product, Engineering, Data Science, UX, Security, and Legal teams to support the adoption of AI and machine learning capabilities across products and platforms.
- Create technical documentation, sample applications, tutorials, and implementation guides to help engineers transition from traditional software development to AI\-powered application development.
Partner with engineering teams to introduce modern AI engineering practices, reusable tooling, and machine learning workflows into existing software development processes.
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Bring others with you
- Partner closely with data scientists, machine learning engineers, designers, product managers, legal SMEs, and platform engineering teams. Effective AI product development depends on strong cross\-functional collaboration and respect for each discipline’s expertise.
- Collaborate with and support engineering teams in adopting modern AI engineering practices, agent workflows, and evaluation approaches.
- Communicate clearly with people who aren’t engineers — especially lawyers — and adapt your language to the audience without dumbing things down.
- Contribute feedback and implementation learnings to shared AI platform capabilities, tooling, and developer workflows.
Contribute constructively to technical discussions, collaborate effectively across teams, and remain open to feedback and evolving implementation approaches.
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Required qualifications
- 6\+ years of experience as a Software Engineer, AI Engineer, Platform Engineer, or related technical role.
- Strong production experience building LLM\-powered applications and deployment at scale.
- Strong programming skills in Python and experience building scalable production services and APIs.
- Experience designing and implementing AI application architectures in cloud\-native environments.
- Hands\-on experience with modern AI engineering frameworks and tooling such as LangChain, LangGraph, LlamaIndex, OpenAI APIs, Anthropic APIs, MCP, or equivalent systems.
- Experience building AI workflows involving retrieval, tool calling, orchestration, context management, and structured generation.
- Familiarity with AI observability, evaluation frameworks, and production monitoring.
- Experience deploying and operating AI systems on AWS, Azure, or GCP.
- Comfortable working in evolving environments and collaborating across teams to deliver AI\-powered features and workflows.
- Strong communication and collaboration skills with the ability to work effectively across engineering, product, and business teams.
Experience contributing to production systems and collaborating on practical implementation trade\-offs.
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Preferred qualifications
- Experience in legal technology, enterprise SaaS, compliance, financial services, healthcare, or other regulated industries.
- Experience building AI copilots, AI assistants, workflow automation systems, or multi\-agent platforms.
- Familiarity with developer platforms, SDK development, API productization, or AI platform engineering.
- Experience facilitating technical workshops, hackathons, or developer enablement initiatives.
- Strong understanding of AI UX and conversational workflow system design.
- Experience with AI evaluation, guardrails, policy enforcement, and responsible AI deployment.
- Familiarity with inference optimization, LLM serving infrastructure, or AI infrastructure tooling.
- Full\-stack or frontend engineering experience for rapid prototyping and developer experience optimization.
Open\-source contributions, technical blogging, conference speaking, or AI engineering community involvement.
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About LexisNexis Legal \& Professional
LexisNexis Legal \& Professional is a global leader in legal information and analytics, serving customers in more than 150 countries. We are investing aggressively in generative AI, agentic systems, and AI\-native workflows that help legal professionals research faster, draft with confidence, and make better decisions in complex legal environments.
Our mission is to build trustworthy enterprise\-grade AI systems that combine cutting\-edge innovation with the accuracy, transparency, and reliability required in the legal industry.
LexisNexis is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
\&\#xa;\&\#xa;U.S. National Base Pay Range: $115,400 \- $192,300\. Geographic differentials may apply in some locations to better reflect local market rates.\&\#xa;\&\#xa;\&\#xa;\&\#xa;This job is eligible for an annual incentive bonus.\&\#xa;\&\#xa;
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 $115K-$192K range is below the median 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 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($153K) sits 14% below the category median. Disclosed range: $115K to $192K.
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.
LexisNexis Legal & Professional AI Hiring
LexisNexis Legal & Professional has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Raleigh, NC, US. Compensation range: $192K - $219K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 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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