Applied AI Engineer

Responsibilities

• Build, prototype, and iterate on AI-powered applications, agent workflows, and scalable LLM-powered experiences for legal and professional use cases.
• Partner with customers and internal stakeholders to translate complex workflows and pain points into technical problem statements and prototypes.
• Implement LLM capabilities including prompt engineering, multi-step workflows, tool calling, and lightweight agent patterns.
• Contribute to scalable orchestration layers for prompting, retrieval, and tool integration across AI services.
• Support AI engineering enablement by promoting reusable standards, tooling, and best practices to help software teams integrate generative AI.
• Collaborate cross-functionally with Data Science, Product, UX, and Engineering teams to drive AI adoption and product quality.

Requirements

• 6+ years of experience in software engineering, AI engineering, or a related technical role.
• Proven production experience building and deploying LLM-powered applications at scale.
• Proficiency in Python and experience building scalable production services and APIs.
• Hands-on experience with modern AI frameworks such as LangChain, LangGraph, LlamaIndex, or OpenAI/Anthropic APIs.
• Experience designing AI application architectures in cloud-native environments (AWS, Azure, or GCP).
• Experience building AI workflows involving retrieval (RAG), tool calling, orchestration, and context management.

Preferred Qualifications

• Experience in legal technology, enterprise SaaS, or other highly regulated industries.
• Experience building AI copilots, assistants, or multi-agent platforms.
• Familiarity with AI observability, evaluation frameworks, and production monitoring.
• Knowledge of AI UX and conversational workflow system design.
• Experience with inference optimization or LLM serving infrastructure.

About the Company

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 to help legal professionals research faster and make better decisions in complex environments.