Ai Rag Jobs in Dallas, TX (NOW HIRING)

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 ...

Architect and build AI solutions using LLMs, Agentic AI, RAG, MCP/tool-calling, APIs, and enterprise integrations . * Develop full-stack applications, including backend services, APIs, frontend ...

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 ...

... AI RAG Design and deploy sophisticated RAG pipelines and Transformerbased architecture Orchestrate LLMasaService eg GPT4 GeminiVertex AI alongside local SLMs eg Llama 3 Mixtral Agentic Workflows ...

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 & RAG: Design and deploy sophisticated RAG pipelines. Orchestrate LLM-as-a-Service (e.g., GPT-4, Gemini, Vertex AI) alongside local SLMs (e.g., Llama 3, Mixtral). * Agentic Workflows:

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.

Hands-on experience with LLMs, Agentic AI, RAG, LangChain, LangGraph, or MCP * Experience developing and deploying production AI applications * Strong understanding of software engineering principles ...

Drive adoption of AI-enabled, agentic, and cloud-native architectures, including AI/ML, RAG, LLM integration, reasoning models, and intelligent workflow orchestration to improve automation ...

Azure Machine Learning, Azure OpenAI, Azure AI Foundry Experience building MLOps platforms and automated ML pipelines Strong knowledge of LLMOps, LLM lifecycle management, agentic AI, RAG (retrieval ...

Agentic AI & RAG Integration * Design, build, and deploy multi-agent orchestration frameworks (using LangChain, AutoGen, or CrewAI) that communicate, self-correct, and execute complex workflows.

RAG & Vector Databases * LangGraph, Agents SDK, Google ADK * Docker, Kubernetes, CI/CD * AWS, Azure, or GCP * AI Governance & Observability * Cursor, Windsurf, Codex Key Responsibilities * Define AI ...

... RAG, fine-tuning, prompt engineering, function calling, and tool use. • Implement guardrails, evaluation frameworks, and responsible AI controls to ensure production-grade reliability and safety ...

Domain: Enterprise AI / Agentic AI / AWS Bedrock Compensation: Competitive, commensurate with ... Implement advanced RAG pipelines with optimized chunking, reranking, grounding, and hybrid/vector ...

We are seeking an AI ML Engineer to design, develop, and deploy Generative AI applications, with a focus on Retrieval-Augmented Generation (RAG) and agentic workflows. The ideal candidate will have ...

Design end-to-end AI solutions spanning Generative AI (RAG, CAG, GraphRAG, fine-tuning, model distillation) and agentic AI (tool-using agents, multi-agent orchestration, MCP-based integrations)

Design and develop enterprise Generative AI applications using LLMs, RAG, Graph RAG, and multi-agent architectures. * Build scalable document ingestion, embedding, retrieval, and vector search ...