Retrieval Augmented Generation Rag Jobs in Santa Clara, CA

Focuses on implementing retrieval-augmented generation (RAG) pipelines, integrating LLMs with structured/unstructured data sources, and fine-tuning models for specific use cases. Key Skills:

Implement Retrieval-Augmented Generation (RAG) architectures with Oracle Database. * Create and manage vector embeddings and vector indexes. * Integrate Oracle Database with LLMs and Generative AI ...

Experience architecting and implementing Retrieval-Augmented Generation (RAG) pipelines to enhance model performance using external knowledge sources, including document chunking, embedding ...

Strong technical expertise in large language models (LLMs), Retrieval-Augmented Generation (RAG), NLP, and distributed systems. Demonstrated ability to define long-term technical strategy while ...

... Retrieval-Augmented Generation (RAG) - Vector databases - Prompt engineering - Large Language Models (LLMs) - Application: Send suitable profiles and contact details to rams@vensoft.com

Strong technical expertise in large language models (LLMs), Retrieval-Augmented Generation (RAG), NLP, and distributed systems. Demonstrated ability to define long-term technical strategy while ...

Implement and optimize Retrieval-Augmented Generation (RAG) pipelines that combine structured and unstructured enterprise data sources. * Partner with AI engineers, platform teams, and product ...

Design and execute validation strategies for AI/ML and Retrieval-Augmented Generation (RAG) features, including model quality, grounding, and safety evaluation. Major Responsibilities or Assigned ...

The Gen AI Project Manager will lead the design, development, and delivery of generative AI solutions, including Retrieval-Augmented Generation (RAG) pipelines, custom AI agents, multi-agent ...

... Retrieval-Augmented Generation (RAG) pipelines to enhance model performance using external knowledge sources, including document chunking, embedding generation, and retrieval systems. • Design ...

Expertise in building Retrieval-Augmented Generation (RAG) pipelines * Knowledge of semantic layers and knowledge graphs for structured reasoning * Proficient in Python and FastAPI for backend and ...

Guided teams on LLM usage patterns, including prompt design, grounding strategies, and Retrieval-Augmented Generation (RAG) * Collaborated with AI architects and MLOps teams to ensure Responsible AI ...

Architect and implement Retrieval-Augmented Generation (RAG) pipelines to enhance model performance using external knowledge sources, including document chunking, embedding generation, and retrieval ...

Build and scale Retrieval-Augmented Generation (RAG) pipelines, multi-agent orchestrations, and API integrations connecting LLMs to internal databases and enterprise software. * Evaluations ...

You will lead end-to-end engineering and research efforts spanning large-scale Retrieval-Augmented Generation (RAG), world knowledge understanding, conversational reasoning, knowledge generation and ...

The ideal candidate should have hands-on expertise with Retrieval-Augmented Generation (RAG), Agentic AI workflows, and LLM-based automation, with the ability to integrate AI systems into complex ...

Develop tools for training and fine-tuning large language models, advanced Retrieval-Augmented Generation (RAG) pipelines, vector databases and agentic frameworks. * Build efficient data pipelines to ...

Contribute to architecture, retrieval-augmented generation (RAG) design, prompt engineering, and CI/CD practices that support rapid, reliable delivery of AI features. Succeed in this role by shipping ...

Preferred Qualifications Strong background and experience in Machine Learning, NLP, and RAG. Strong ... D. in Computer Science, Machine Learning, information retrieval, data mining, or a related field 8+ ...

Develop tools for training and fine-tuning large language models, advanced Retrieval-Augmented Generation (RAG) pipelines, vector databases and agentic frameworks. * Build efficient data pipelines to ...