Applied AI Engineer

Reed Technology•Raleigh, NC

1d•$115,400 - $192,300

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.