Design, build, and optimize generative AI, RAG, and agentic RAG systems for drafting, search, and reasoning. Improve LLM outputs via prompt design, retrieval optimization, embeddings, reranking, and evaluation. Develop production-grade ML components and retrieval pipelines using OpenSearch/Solr, write maintainable Python code, and collaborate with engineering, product, and domain teams to improve relevance, accuracy, and user trust.
Are you excited to shape the next generation of AI‑powered drafting and search experiences at scale?
Do you want to work on advanced Generative AI systems that turn complex information into clear, trusted insights?
About our Team
You will join a multidisciplinary team of data scientists, engineers, and product partners focused on building intelligent, reliable, and responsible AI solutions for legal and business professionals. We work collaboratively, value thoughtful experimentation, and prioritize high‑quality, production‑ready outcomes.
You will join a multidisciplinary team of data scientists, engineers, and product partners focused on building intelligent, reliable, and responsible AI solutions for legal and business professionals. We work collaboratively, value thoughtful experimentation, and prioritize high‑quality, production‑ready outcomes.
About the Role
As a Senior Data Scientist II, you will design and improve Generative AI, Retrieval‑Augmented Generation (RAG), and agentic RAG systems that power drafting, search, and reasoning use cases. You will focus on retrieval quality, evaluation, and production‑grade machine learning components, working closely with cross‑functional partners to deliver measurable improvements in relevance, accuracy, and user trust.
As a Senior Data Scientist II, you will design and improve Generative AI, Retrieval‑Augmented Generation (RAG), and agentic RAG systems that power drafting, search, and reasoning use cases. You will focus on retrieval quality, evaluation, and production‑grade machine learning components, working closely with cross‑functional partners to deliver measurable improvements in relevance, accuracy, and user trust.
Responsibilities
- Design, build, and optimize Generative AI, RAG, and agentic RAG systems for drafting, search, and reasoning use cases
- Improve LLM‑generated outputs through prompt design, retrieval optimization, evaluation, and experimentation
- Develop and maintain scalable data science and machine learning components for production environments
- Build and enhance retrieval pipelines, including query processing, document retrieval, ranking, reranking, and grounding strategies
- Apply text search and relevance tuning techniques using platforms such as OpenSearch and Solr
- Use embeddings, hybrid retrieval, semantic search, and reranking methods to improve answer quality and system performance
- Write high‑quality, maintainable, production‑ready Python code following strong software engineering practices
- Collaborate closely with partners across data science, engineering, product, and domain teams
Requirements
- Proven experience building and improving Generative AI and LLM‑based solutions in production or near‑production settings
- Strong experience with RAG frameworks, retrieval‑based AI architectures, and agentic workflows
- Advanced Python skills, including modular design, testing, debugging, and clean coding practices
- Strong understanding of information retrieval, text search methods, and retrieval architectures
- Hands‑on experience with OpenSearch, Solr, or similar search platforms
- Experience with embeddings, semantic retrieval, reranking, and relevance optimization
- Proven ability to work effectively in a monorepo environment with disciplined version control and code review practices
- Experience collaborating in cross‑functional teams and contributing to CI/CD and deployment workflows
Work in a Way That Works for You
We promote flexible ways of working that support collaboration, focus, and personal wellbeing, while meeting the needs of our teams and customers.
We promote flexible ways of working that support collaboration, focus, and personal wellbeing, while meeting the needs of our teams and customers.
Working Pattern
This role supports flexible working arrangements, balancing individual productivity with team collaboration.
This role supports flexible working arrangements, balancing individual productivity with team collaboration.
About the Business
You will be part of the LNLP division, within the Standard business unit, delivering trusted information and technology solutions that help professionals make confident decisions.
You will be part of the LNLP division, within the Standard business unit, delivering trusted information and technology solutions that help professionals make confident decisions.
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Evanston, United States
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