LexisNexis is looking to leverage advanced AI, specifically LLMs and RAG architectures, to build intelligent systems that can assist legal professionals by generating legal documents, interpreting contracts, and autonomously executing multi-step tasks within complex legal workflows. The goal is to enhance productivity and transform the legal market by providing AI-powered legal assistants.
Requirements
- Previous people management experience (3+ years) of teams building LLM-based applications, especially in domains requiring rigor and traceability like legal, finance, or compliance.
- Hands-on experience delivering systems using LangChain, LLM agent frameworks, vector databases, and prompt orchestration pipelines.
- Strong background in deploying retrieval-augmented generation (RAG) systems for grounded and auditable responses.
- Demonstrated success in taking LLM-powered applications from prototype to production.
- Proficient with Python and LLM tooling: LangChain, HuggingFace Transformers, OpenAI APIs, and prompt tuning techniques.
- Familiarity with vector databases (e.g., Solr, Elasticsearch, Qdrant, Weaviate), knowledge graphs, and hybrid retrieval architectures.
- Working knowledge of containerization (Docker, Kubernetes), CI/CD, and model serving tools (TorchServe, Triton, Ray Serve).
Responsibilities
- Architect systems that combine foundation models with RAG pipelines to draft contracts, memos, and pleadings grounded in enterprise knowledge.
- Guide the development of AI agents that perform multi-step reasoning, cite evidence, retrieve domain-specific content, and produce compliant outputs.
- Lead evaluation of model performance for generation, summarization, classification, and dialogue-based workflows with legal constraints in mind.
- Build scalable prompt engineering frameworks and manage fine-tuning or adaptation of models to legal datasets.
- Oversee development of infrastructure to support continuous delivery and monitoring of LLM systems in production environments.
Other
- Define the roadmap for applying LLMs and agentic AI to real-world legal drafting, summarization, and task automation challenges.
- Evangelize and embed AI-driven change across legal product platforms.
- Translate evolving generative AI capabilities into tangible customer value within the legal domain.
- Mentor and grow a multidisciplinary team of LLM-focused Data Scientists and ML Engineers.
- Drive cross-functional collaboration with Legal SMEs, Data Engineers, Product Managers, and Design.
