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Retrieval Augmented Generation Rag Jobs in Dallas, TX

AI/ML Engineer

Plano, TX · On-site

$109K - $131K/yr

Fine-tune foundation models and implement Retrieval-Augmented Generation (RAG) architectures. * Develop REST APIs and microservices using FastAPI, Flask, or similar frameworks to expose AI models.

... Retrieval-Augmented Generation (RAG) pipelines, and Agent SDKs - Skilled in building and deploying AI/LLM systems in production environments - Familiarity with AI agents, including evaluation ...

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search technologies * Create, test, and refine prompts, structured outputs, and evaluation ...

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search technologies * Create, test, and refine prompts, structured outputs, and evaluation ...

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search technologies * Create, test, and refine prompts, structured outputs, and evaluation ...

In this role, you will design, build, and enhance intelligent applications thatleverageLarge Language Models (LLMs), document processing pipelines, Retrieval-Augmented Generation (RAG) architectures ...

AI Lead Engineer (Remote)

Dallas, TX · Remote

$104K - $138K/yr

Retrieval-Augmented Generation (RAG) * Embeddings & Vector Databases * Fine-tuning LLMs * Python * TensorFlow * PyTorch * Scikit-learn * Data Preprocessing & Feature Engineering * Model Training ...

AI Lead Engineer

Dallas, TX · On-site +1

$101K - $133K/yr

Retrieval-Augmented Generation (RAG) * Embeddings & Vector Databases * Fine-tuning LLMs * Python * TensorFlow * PyTorch * Scikit-learn * Data Preprocessing & Feature Engineering * Model Training ...

AI Lead Engineer

Dallas, TX · Remote

$101K - $133K/yr

... Retrieval-Augmented Generation (RAG) Embeddings & Vector Databases Fine-tuning LLMs Python TensorFlow PyTorch Scikit-learn Data Preprocessing & Feature Engineering Model Training, Validation ...

Gen. AI Engineer

Fort Worth, TX · On-site

$100K - $160K/yr

Experience building Retrieval-Augmented Generation (RAG) solutions and working with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search. * Experience with Agentic AI ...

Senior AI Engineer

Dallas, TX · On-site

$103K - $142K/yr

Design and deliver LLM-powered applications, including agentic multi-step workflows, Retrieval-Augmented Generation (RAG) systems, and structured prompt pipelines. Productionize AI: Transform AI ...

AI Lead Engineer

Dallas, TX · On-site

$150 - $210/hr

Retrieval-Augmented Generation (RAG) * Embeddings & Vector Databases * Fine-tuning LLMs * Python * TensorFlow * PyTorch * Scikit-learn * Data Preprocessing & Feature Engineering * Model Training ...

New

Architect Retrieval-Augmented Generation ( RAG ) systems, including vector store design, hybrid search strategies, chunking pipelines, and context relevance evaluation. * Apply MLOps best practices ...

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Retrieval Augmented Generation Rag information

See Dallas, TX salary details

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How much do retrieval augmented generation rag jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for retrieval augmented generation rag in Dallas, TX is $20.03, according to ZipRecruiter salary data. Most workers in this role earn between $17.12 and $20.91 per hour, depending on experience, location, and employer.
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Infographic showing various Retrieval Augmented Generation Rag job openings in Dallas, TX as of June 2026, with employment types broken down into 82% Full Time, and 18% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $41,666 per year, or $20 per hour.

$109K - $131K/yr

Full-time

Posted 23 days ago


Job description


AI/ML Engineer
?? Location: Plano, TX (Hybrid)
?? Duration: Long-Term Contract
Client: EmergerTech - USLBM
Job Overview
We are seeking a highly skilled AI/ML Engineer with strong expertise in Machine Learning, Deep Learning, Generative AI, and Large Language Models (LLMs) to design, build, and deploy enterprise-scale AI solutions. The ideal candidate will have hands-on experience developing production-ready AI applications, implementing MLOps best practices, and building scalable cloud-based machine learning systems.
This role offers the opportunity to work on cutting-edge AI initiatives, including Generative AI, Retrieval-Augmented Generation (RAG), LLM fine-tuning, and intelligent enterprise applications.
Key Responsibilities
  • Design, develop, train, and deploy scalable Machine Learning and Deep Learning models for production environments.
  • Build AI-powered applications using traditional ML techniques, Generative AI, and Large Language Models (LLMs).
  • Develop and optimize data pipelines for structured and unstructured data processing.
  • Fine-tune foundation models and implement Retrieval-Augmented Generation (RAG) architectures.
  • Develop REST APIs and microservices using FastAPI, Flask, or similar frameworks to expose AI models.
  • Build, automate, and maintain MLOps pipelines for model training, deployment, monitoring, and lifecycle management.
  • Collaborate with Data Scientists, Software Engineers, Data Engineers, and business stakeholders to deliver AI-driven solutions.
  • Optimize model accuracy, scalability, inference performance, and latency.
  • Implement AI governance, responsible AI practices, model monitoring, and security best practices.
  • Stay current with emerging AI technologies, frameworks, and industry trends.

Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
  • 10+ years of hands-on experience in Artificial Intelligence, Machine Learning, or Data Science.
  • Strong programming experience with Python.
  • Experience with Machine Learning frameworks including TensorFlow, PyTorch, and Scikit-learn.
  • Hands-on experience with Large Language Models (LLMs), Prompt Engineering, and Generative AI.
  • Experience implementing Retrieval-Augmented Generation (RAG) solutions.
  • Knowledge of vector databases such as Pinecone, FAISS, Chroma, or Milvus.
  • Experience with MLOps platforms such as MLflow, Kubeflow, SageMaker, or Vertex AI.
  • Experience building REST APIs using FastAPI, Flask, or similar frameworks.
  • Strong understanding of SQL and NoSQL databases.
  • Hands-on experience with Docker, Kubernetes, Git, and CI/CD pipelines.
  • Experience working with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
  • Excellent analytical, communication, and problem-solving skills.

Preferred Qualifications
  • Experience with LangChain, LlamaIndex, or Semantic Kernel.
  • Experience working with Azure OpenAI, OpenAI APIs, Anthropic Claude, or Google Gemini.
  • Knowledge of distributed computing frameworks such as Apache Spark.
  • Experience with Databricks or Snowflake.
  • Familiarity with Responsible AI, AI Governance, and Model Monitoring.
  • AI/ML or Cloud certifications are a plus.

Technical Skills
  • Python
  • Machine Learning
  • Deep Learning
  • Generative AI
  • Large Language Models (LLMs)
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • LangChain / LlamaIndex
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • FastAPI / Flask
  • Docker
  • Kubernetes
  • MLflow / Kubeflow
  • SQL / NoSQL
  • Vector Databases (Pinecone, FAISS, Chroma, Milvus)
  • REST APIs
  • Git
  • CI/CD
  • AWS / Azure / Google Cloud Platform