Ai Rag Jobs in Dallas, TX (NOW HIRING)

InterSources Inc is focused on developing reusable toolkits for Gen AI RAG capabilities. The role involves designing, building, and operating software solutions while ensuring compliance and ...

Contract Key Skills - AI, Python, Rag, LLM Overview We are seeking an AI Engineer with proven experience in building and scaling AI-powered applications . This role combines hands-on development with ...

Implement Graph RAG solutions by integrating knowledge graphs, entity extraction, relationship ... Deploy AI workloads on AWS, Azure, and GCP , using services such as Bedrock, SageMaker, Azure ...

RAG * Agentic AI / Multi-Agent Systems * MCP (Model Context Protocol) * Production deployment experience * End-to-end ownership (Discovery → Production)

Implement RAG pipelines, including chunking strategies, embedding models, and vector store integration (Pinecone, OpenSearch, pgvector, FAISS) * Build and orchestrate AI agents using frameworks such ...

Implement RAG pipelines, including chunking strategies, embedding models, and vector store integration (Pinecone, OpenSearch, pgvector, FAISS) * Build and orchestrate AI agents using frameworks such ...

Google Cloud Platform Generative AI LLMs Agentic AI Multi-Agent / A2A Systems Complex Chatbots / Conversational AI RAG Knowledge Graphs Machine Learning MCP Python Enterprise AI Architecture Document ...

Define reference architectures for LLM, RAG, Generative AI, and multi-agent workflows . * Design solutions using structured and unstructured data, including RAG and Action Agents for task automation.

Implement RAG-based architectures connecting LLMs with structured and unstructured enterprise data. * Develop and test AI agents, traditional ML models, and deterministic logic for real-world use ...

Implement RAG-based architectures connecting LLMs with structured and unstructured enterprise data. * Develop and test AI agents, traditional ML models, and deterministic logic for real-world use ...

Implement RAG-based architectures connecting LLMs with structured and unstructured enterprise data. * Develop and test AI agents, traditional ML models, and deterministic logic for real-world use ...

Implement RAG-based architectures connecting LLMs with structured and unstructured enterprise data. * Develop and test AI agents, traditional ML models, and deterministic logic for real-world use ...

Generative AI, Agentic AI, RAG, AI Agents * AI orchestration frameworks (such as LangChain or LangGraph) * Vector databases and knowledge retrieval * AI governance, security, and Responsible AI

Generative AI, Agentic AI, RAG, AI Agents * AI orchestration frameworks (such as LangChain or LangGraph) * Vector databases and knowledge retrieval * AI governance, security, and Responsible AI

RAG (Retrieval-Augmented Generation) Establish best practices for prompt engineering, agent memory, tool usage, context management, reasoning, and AI governance. Lead technical decision-making for ...

Lead the end-to-end design, development, and implementation of scalable AI/ML systems and pipelines, emphasizing Generative AI/LLM solutions, including RAG implementation and evaluation.

Generative AI, Agentic AI, RAG, AI Agents * AI orchestration frameworks (such as LangChain or LangGraph) * Vector databases and knowledge retrieval * AI governance, security, and Responsible AI

Define and own Acosta Group's enterprise AI reference architecture across traditional AI, GenAI, agentic AI, copilots, RAG, orchestration, workflow automation, and AI-enabled enterprise applications.

Design and implement AI-ready data architectures that support analytics, machine learning, generative AI, RAG, vector databases, and Agentic AI solutions. * Leverage AI-assisted development tools ...

Conduct discovery, threat modeling, and adversarial testing on generative AI, RAG pipelines, ML systems, and AI agents to identify vulnerabilities such as prompt injection, jailbreaking, data ...