Senior AI Engineer - Retrieval-Augmented Gen & Vector Search

LexisNexis; México; Remote; MXN 900,000 - 1,400,000; 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. You will work on document ingestion, embedding generation, retrieval, and source-grounded responses to ensure trustworthy results.

You will design vector search workflows, validate citation accuracy, and collaborate with teams to reduce hallucinations while improving latency and cost efficiency across regulated content domains.

Qualifications

• Hands-on experience with Retrieval-Augmented Generation (RAG) applications, including document ingestion, chunking, embedding generation, retrieval, reranking, and source-grounded responses.
• Strong understanding of Generative AI and LLM capabilities, limitations, hallucination mitigation, and response-quality evaluation.
• Experience designing and operating vector search solutions using Qdrant, Pinecone, pgvector, LanceDB, OpenSearch vector search, or equivalent technologies.
• Ability to validate retrieval quality, citation accuracy, relevance, coverage, and factual grounding using reproducible evaluation datasets.

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

Generative AI concepts

Vector search technologies

Evaluation datasets

Tools

Qdrant

Pinecone

pgvector

LanceDB

OpenSearch vector search

Job description

LexisNexis Legal & Professional is seeking an AI Engineer focused on Retrieval-Augmented Generation (RAG) to design, build, and optimize AI-powered experiences. You will work on document ingestion, embedding generation, retrieval, and source-grounded responses to ensure trustworthy results.

You will design vector search workflows, validate citation accuracy, and collaborate with teams to reduce hallucinations while improving latency and cost efficiency across regulated content domains.