Senior RAG AI Engineer - Retrieval & Grounded AI

Aimlroles; Ciudad de México; On-site; MXN 800,000 - 1,200,000; Full time

Job summary

LexisNexis Legal & Professional is seeking an AI Engineer focused on Retrieval-Augmented Generation (RAG) to design, build, and optimize AI-powered experiences in legal and professional information products.

You will work with advanced AI technologies, validate retrieval quality, and collaborate with cross-functional teams to reduce hallucinations and ensure source-grounded responses.

Qualifications

• Hands-on experience with Retrieval-Augmented Generation (RAG) applications including ingestion, chunking, and embedding workflows.
• Ability to validate retrieval quality, citation accuracy, and factual grounding using reproducible datasets.
• Experience with vector search technologies and hybrid search strategies.

Responsibilities

• Design, build, and optimize Retrieval-Augmented Generation (RAG) applications.
• Develop and manage document ingestion, chunking, embedding generation, retrieval, reranking, and source-grounded response workflows.
• Design and operate vector search solutions using modern vector database technologies.
• Validate retrieval quality, citation accuracy, relevance, coverage, and factual grounding using reproducible evaluation datasets.
• Collaborate with cross-functional teams to improve response quality and reduce hallucinations.
• Implement hybrid search strategies combining keyword, metadata, semantic/vector search, and reranking techniques.
• Monitor and evaluate retrieval performance, latency, cost, and AI system quality.
• Support the development of scalable and responsible Generative AI solutions across legal and professional information products.

Skills

RAG applications

Document ingestion

Chunking

Embedding generation

Retrieval

Reranking

Vector search

Quality evaluation

Hybrid search

LangChain

Tools

Qdrant

Pinecone

pgvector

LanceDB

OpenSearch

Job description

LexisNexis Legal & Professional is seeking an AI Engineer focused on Retrieval-Augmented Generation (RAG) to design, build, and optimize AI-powered experiences in legal and professional information products.

You will work with advanced AI technologies, validate retrieval quality, and collaborate with cross-functional teams to reduce hallucinations and ensure source-grounded responses.