Senior LLM Engineer (RAG & AWS Bedrock)

Join Our Team Oowlish, one of Latin America's rapidly expanding software development companies, is seeking experienced technology professionals to enhance our diverse and vibrant team. As a valued member of Oowlish, you will collaborate with premier clients from the United States and Europe, contributing to pioneering digital solutions. Our commitment to creating a nurturing work environment is recognized by our certification as a Great Place to Work, where you will have opportunities for professional development, growth, and a chance to make a significant international impact. We offer the convenience of remote work, allowing you to craft a work-life balance that suits your personal and professional needs. We're looking for candidates who are passionate about technology, proficient in English, and excited to engage in remote collaboration for a worldwide presence.

We are looking for an experienced Senior LLM Engineer to build an AI-powered data intelligence platform that enables users to interact with multiple enterprise data sources through natural language. This role focuses on designing and implementing Retrieval-Augmented Generation (RAG) solutions, semantic search, and conversational AI experiences powered by AWS Bedrock. You will build scalable AI pipelines capable of retrieving, processing, and synthesizing information from multiple structured and unstructured data sources into an intuitive chatbot experience. The ideal candidate has strong experience building Generative AI applications, designing RAG architectures, and working with vector search, embeddings, and cloud-native AI services.

: • Design and implement Retrieval-Augmented Generation (RAG) architectures. • Build AI-powered chatbot experiences using AWS Bedrock. • Develop multi-source data ingestion and retrieval pipelines. • Design and optimize embeddings and semantic search strategies. • Build scalable vector search solutions for enterprise knowledge retrieval. • Integrate structured and unstructured data sources into AI workflows. • Develop prompt engineering strategies to improve response quality and accuracy. • Build cloud-native AI services using AWS. • Collaborate with engineering teams to improve AI performance, scalability, and reliability. • Continuously evaluate new AI technologies and best practices for enterprise LLM applications.