Retrieval Augmented Generation Rag Jobs in Arizona

Candidates should have practical experience building GenAI applications using LLMs, Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, AI agents, and enterprise AI governance.

... Retrieval-Augmented Generation (RAG), and Agentic AI technologies. Working within a highly regulated financial environment, you will contribute to building scalable data solutions and support AI ...

... retrieval-augmented generation (RAG), orchestration frameworks, and scalable AI services. You will work closely with business stakeholders and engineering teams to deliver reusable AI capabilities ...

Build intelligent solutions utilizing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and workflow orchestration technologies. * Develop scalable backend services, REST ...

Build intelligent solutions utilizing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and workflow orchestration technologies. * Develop scalable backend services, REST ...

... retrieval-augmented generation (RAG), orchestration frameworks, and scalable AI services. You will work closely with business stakeholders and engineering teams to deliver reusable AI capabilities ...

... Skills: - Retrieval-Augmented Generation (RAG) - Prompt engineering and optimization - Data Modeling - Synthetic Data Generation - Exploratory Data Analysis - Data cleaning and preprocessing ...

... retrieval-augmented generation (RAG) pipelines over proprietary data to keep outputs grounded and accurate · Integrate LLM capabilities into internally built (homegrown) applications via APIs ...

... retrieval-augmented generation (RAG), prompt engineering, and agentic AI frameworks. Partner with Security, Risk, Engineering, Governance, and Data teams to deliver secure, reliable, scalable, and ...

Develop agent-based solutions using modern AI frameworks, Retrieval-Augmented Generation (RAG), GraphRAG, and Model Context Protocol (MCP). * Create and maintain streaming and batch data pipelines ...

AI/ML & RAG: Practical experience building Generative AI and Retrieval-Augmented Generation (RAG) solutions, including vector databases, embeddings, and retrieval pipelines. Understands model ...

Build and maintain content vectorization and retrieval-augmented generation (RAG) pipelines that give AI agents access to relevant financial context, including prior work product, regulatory guidance ...

Data & Retrieval: Proficiency in vector databases (Pinecone, Weaviate, Chroma, Milvus) and building Retrieval-Augmented Generation (RAG) or GraphRAG pipelines. * Agentic Workflows: Designing multi ...

Build and maintain content vectorization and retrieval-augmented generation (RAG) pipelines that give AI agents access to relevant financial context, including prior work product, regulatory guidance ...

Design and implement RAG (Retrieval-Augmented Generation) pipelines, integrate MCP (Model Context Protocol) servers/tools, vector databases, and enterprise LLM orchestration framework (e.g., Spring ...

Design, build and rollout production-grade AI solutions, including LLM-powered applications, retrieval-augmented generation (RAG) systems, ensuring high standards of accuracy, reliability, and ...

Integrate with large language models (LLMs) using prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) techniques. * Implement MCP client and server within the Grafana ecosystem ...

Help implement and maintain retrieval-augmented generation (RAG) pipelines over financial data, with an emphasis on correctness and safety. * Contribute to shared AI infrastructure such as LLM ...

Build and optimize RAG (Retrieval-Augmented Generation) pipelines using vector databases and embedding models. * Integrate LLMs such as GPT, Claude, Gemini, Llama, and other foundation models into ...

... Retrieval Augmented Generation (RAG) using vector search and enterprise knowledge sources. • Continuously optimize prompts for accuracy, consistency, latency, and cost efficiency. • Build and ...