Jobs at LexisNexis

Senior consultative pre-sales role partnering with account teams to research, qualify, and progress complex enterprise legal-technology opportunities. Engage senior legal stakeholders, lead discovery and workflow analysis, design tailored demonstrations and proof-of-concepts, and share customer insights with product and GTM teams to drive new and expanded revenue.

The Director of Data Sciences leads a team in strategic initiatives, drives machine learning and AI solutions, and manages resources to enhance data-driven decision making across the business unit.

Build and productionize LLM-powered applications, agent workflows, and RAG pipelines for legal workflows. Engage customers to define problems, prototype solutions, and enable engineering teams with reusable AI patterns, observability, and tooling. Collaborate cross-functionally to deploy cloud-native AI systems and improve developer adoption.

Drive new business sales for the InterAction CRM platform by prospecting targeted sectors, managing the full sales cycle, building relationships, creating ROI-driven proposals, and representing the product at events. Maintain pipeline and territory plans, meet revenue targets, and communicate market trends to optimize positioning.

Lead development and execution of Financial Crime Compliance strategy across Africa, identify growth and M&A opportunities, partner with Product, Sales, Marketing and Professional Services to drive revenue, and provide market, regulatory and competitive insights to inform product and go-to-market plans.

Lead architecture and development of legal tech solutions, act as technical lead with external partners, design data models, resolve complex bugs and performance issues, mentor developers, and drive cross-functional project execution.

Lead multiple agile engineering teams building an Enterprise Legal Management platform. Drive cross-team delivery, provide technical oversight, ensure code quality, CI/CD and secure development, champion AI tools, oversee architecture, integrations, hiring, and continuous improvement.

Design, build, deploy, and maintain production-scale ML/LLM systems and MLOps pipelines. Translate product requirements into software designs, implement LLM/Generative AI and agent-driven solutions (RAG, embeddings), integrate models with APIs and microservices, monitor and improve model performance, and collaborate across teams using cloud, distributed computing, and CI/CD practices.

Drive sales performance by designing and delivering enablement programs, optimizing sales processes and CRM use, building performance analytics and dashboards, coaching sales managers, and aligning cross-functional teams to improve revenue, win rates, and time-to-productivity.

Lead end-to-end development and deployment of LLM and advanced NLP models, architect scalable pipelines, ensure model validation, monitoring, and compliance, partner with product and engineering, and mentor junior data scientists.

Own product or major features for a complex legal-technology product using agentic AI. Define vision and roadmap, gather customer insights, drive data-driven decisions and KPIs, write requirements, manage SDLC/agile delivery, run launches and GTM, mentor junior PMs, and collaborate with engineering, data, and commercial teams.

Lead design, optimization, and management of large-scale database systems and backend platforms. Build data models, schemas, indexes, and ETL pipelines; write and tune complex SQL; develop stored procedures and Java data access layers; debug production issues and optimize performance; automate operations with shell scripts in Linux/Unix environments; provide technical leadership and best-practice guidance across data engineering initiatives.

Lead design and deployment of custom legal workflows and vault templates for large law firms. Partner with firm stakeholders and sales/post-sales to define scope, run pilots, drive adoption, surface product gaps, and collaborate with product, engineering, and UX to deliver AI-enabled legal solutions and rollout resources.

Design, build, and maintain scalable data pipelines and integrations to support analytics and AI use cases. Develop well‑tested code, perform complex SQL queries and data transformations, troubleshoot data issues, and collaborate with technical and business partners. Improve automation, performance, and scalability while documenting requirements and supporting data platform reliability and AI readiness.

Designs and develops cross-system software, provides technical design input, mentors junior engineers, troubleshoots complex issues, works directly with customers, and leads integrations including payment services.

Lead modernization of patent data processing workflows by owning roadmaps, designing AI-native automation (LLMs, NLP, CV), running discovery and experiments, defining requirements and success metrics, analyzing operational telemetry, and driving adoption and realized cost/productivity outcomes in a regulated, hybrid environment.

Design, build, and evaluate ML and NLP/LLM solutions on large datasets: data preparation, modeling (including generative models), prompt engineering, model deployment, visualization, and cross-functional collaboration to drive business outcomes.

Design and lead end-to-end AI solutions using LLMs, RAG, agentic systems, vector databases and cloud infrastructure. Advise on AI architecture, MLOps, production readiness, and guide engineering teams to align systems with business goals.

Lead design and validation of agentic AI capabilities for legal workflows using LLMs, NLP, RAG, hybrid search, embeddings and vector databases. Drive experimentation, evaluation, and metrics, translate results to product recommendations, collaborate with engineers and legal experts, and mentor team members.

Build and maintain scalable, cloud-native full‑stack payment systems. Collaborate with engineers and stakeholders to design, implement, secure, and migrate services using Java, Apache Camel, Spring Boot, AWS, and React/Angular. Troubleshoot issues, fix bugs, and support APAC payments, security updates, and migration projects within Agile/Waterfall environments.