RAG AI Engineer (Remote) — Flexible Hours, Global Impact

BairesDev; Colombia; Remote; COP 292,047,000 - 486,744,000; Full time

Benefits offered by this job

Remote work

USD compensation

Home office setup

Flexible hours

Parental leave

Job summary

BairesDev is seeking an AI Engineer specialized in Retrieval-Augmented Generation (RAG) to design and implement advanced RAG systems for grounding responses in external knowledge bases. This role focuses on building scalable ingestion, embedding, and retrieval workflows in a fully remote environment.

You will work with cutting-edge vector databases and collaborate with global teams to deploy enterprise-grade solutions, balancing accuracy and performance across diverse data sources.

Qualifications

• 4+ years of experience in AI Engineering, ML, or Software Engineering.
• Proven expertise in Retrieval-Augmented Generation (RAG) and hybrid search.
• Advanced Python skills and experience with vector databases (Pinecone, Qdrant, Weaviate).
• Hands-on with chunking, embeddings and reranking techniques.
• Familiarity with evaluation frameworks to ensure grounded, high-quality outputs.
• Strong English proficiency.

Responsibilities

• Design and optimize end-to-end RAG pipelines including ingestion, cleaning, chunking, and embeddings.
• Manage high-performance vector databases for efficient semantic and hybrid search.
• Develop chunking and reranking strategies to improve retrieval relevance.
• Benchmark RAG systems using established frameworks for production-grade results.
• Collaborate with data and software teams to integrate RAG into enterprise apps.
• Explore agentic RAG and context compression techniques in new research.

Skills

RAG

Hybrid search

Python

Vector databases

Embeddings

Reranking

English

Tools

Pinecone

Qdrant

Weaviate

Job description

BairesDev is seeking an AI Engineer specialized in Retrieval-Augmented Generation (RAG) to design and implement advanced RAG systems for grounding responses in external knowledge bases. This role focuses on building scalable ingestion, embedding, and retrieval workflows in a fully remote environment.

You will work with cutting-edge vector databases and collaborate with global teams to deploy enterprise-grade solutions, balancing accuracy and performance across diverse data sources.