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Rag Developer Jobs in Addison, TX (NOW HIRING)

Senior Java Developer - AI/ML

Dallas, TX · On-site

$119K - $155K/yr

Implement Retrieval Augmented Generation (RAG) architectures. * Build prompt orchestration and prompt engineering pipelines. * Integrate vector databases for semantic search. * Develop AI agents ...

Oracle AI Developer

Dallas, TX · On-site

$56.25 - $69.75/hr

Oracle AI Developer Location: Dallas, TX & Alpharetta, NJ - Middletown/Bedminster (Onsite ... Building agentic AI solutions, RAG architectures, natural language to SQL capabilities, API-led ...

New

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 ...

Gen. AI Engineer

Fort Worth, TX · On-site

$100K - $160K/yr

Design and develop enterprise Generative AI applications using LLMs, RAG, Graph RAG, and multi ... Hands-on experience with Docker, Kubernetes, Terraform, CI/CD pipelines, and modern DevOps ...

Senior Agentic AI Developer

Coppell, TX · On-site

$50.75 - $67/hr

Experience with RAG architectures, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgVector), prompt engineering, and AI orchestration. * Hands-on experience with AI development tools such ...

AI Engineer

Grand Prairie, TX · On-site

$99K - $133K/yr

AI Engineer Location: Grand-Prairie, TX Experience with MES and SCADA is preferred but not required ... RAG pipelines: use Hugging Face, LangChain, and Open AI API to architect RAG pipelines and ...

AI Developer - Information Technology

Dallas, TX · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

The AI Developer is responsible for designing, building, and maintaining AI agents, copilots, and ... Develop and tune prompts, RAG pipelines, knowledge grounding, and tool/function integrations for ...

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 ...

Python + AI

Addison, TX · On-site

$48.75 - $67/hr

C. is seeking a GenAI / AI Engineer focused on building LLM-based applications, RAG pipelines, and AI APIs using Python and FastAPI. The role also involves ML deployment using Docker, Kubernetes ...

AI Engineer - Grand Prairie, TX

Grand Prairie, TX · On-site

$99K - $133K/yr

... developer). Required Skills: LLM/AI: Hugging Face, LangChain, Open AI API. RAG pipelines: use Hugging Face, LangChain, and Open AI API to architect RAG pipelines and integrate generative AI into ...

AI Engineer Location: Grand-Prairie, Texas 75050 Work Model: Onsite (4 days a week work from office ... RAG pipelines: use Hugging Face, LangChain, and Open AI API to architect RAG pipelines and ...

Extensive knowledge and expertise of leveraging GitHub Copliot/Claude Code/Amazon Q Developer to ... Hands on RAG implementation including vectorDBs

Senior AI Engineer

Dallas, TX · On-site +1

$103K - $142K/yr

Senior AI Engineer Department: Backend Employment Type: Full Time Location: USA Description We're ... You'll design and ship RAG pipelines, integrate LLMs into real products, and build the backend ...

AI/ML Engineer

Plano, TX · On-site

$109K - $131K/yr

AI/ML Engineer ?? Location: Plano, TX (Hybrid) ?? Duration: Long-Term Contract Client: EmergerTech ... Fine-tune foundation models and implement Retrieval-Augmented Generation (RAG) architectures.

Showing results 21-40

Rag Developer information

What is the difference between Rag Developer vs Textile Technician?

AspectRag DeveloperTextile Technician
CredentialsTypically requires a diploma or degree in textiles or related fieldRequires similar qualifications, often with additional certifications in textile testing
Work EnvironmentFactories, textile mills, production plantsLaboratories, quality control departments, manufacturing facilities
Industry UsageUsed in textile manufacturing to develop and process rags for reuse or recyclingInvolved in testing, quality assurance, and technical support in textile production

Both Rag Developers and Textile Technicians work within the textile industry, often in manufacturing settings. Rag Developers focus on creating and processing recycled rags, while Textile Technicians handle testing and quality control. The roles share similar educational backgrounds and work environments, but their specific responsibilities differ based on their focus within textile production.

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Cities near Addison, TX with the most Rag Developer job openings:

Senior Java Developer - AI/ML

Photon

Dallas, TX • On-site

$119K - $155K/yr

Other

Posted 17 days ago


Job description

Job Title: Senior Java Developer AI/LLM Solutions
Location: Dallas, TX (Onsite) 

About the Role

We are seeking an experienced Senior Java Backend Developer with hands-on expertise in building scalable backend applications and integrating AI-powered solutions, Large Language Models (LLMs), and Generative AI technologies. The ideal candidate will have a strong foundation in Java microservices development while leveraging modern AI frameworks such as LangChain, LangGraph, OpenAI APIs, Azure OpenAI, or AWS Bedrock to build intelligent enterprise applications.

This role involves designing, developing, and deploying highly scalable APIs and backend services that power AI-driven business solutions.

Key Responsibilities

Backend Development

  • Design, develop, and maintain enterprise-grade backend applications using Java 17/21.
  • Develop scalable RESTful APIs and Microservices using Spring Boot and Spring Cloud.
  • Build highly available distributed systems following microservices architecture.
  • Implement asynchronous processing using Kafka, RabbitMQ, or JMS.
  • Optimize application performance, scalability, and reliability.
  • Develop secure APIs using OAuth2, JWT, Spring Security, and API Gateway.

AI & LLM Integration

  • Integrate applications with Large Language Models (LLMs) such as:
    • OpenAI GPT
    • Azure OpenAI
    • Anthropic Claude
    • Google Gemini
    • AWS Bedrock
    • Llama Models
  • Develop AI-powered backend services for:
    • Intelligent search
    • Chatbots
    • AI Assistants
    • Document Processing
    • Knowledge Management
    • Code Generation
    • Summarization
    • Recommendation Engines
  • Implement Retrieval Augmented Generation (RAG) architectures.
  • Build prompt orchestration and prompt engineering pipelines.
  • Integrate vector databases for semantic search.
  • Develop AI agents using modern multi-agent frameworks.
  • Monitor AI model performance and optimize prompts for accuracy and cost.

AI Frameworks

Hands-on experience with one or more:

  • LangChain
  • LangGraph
  • Spring AI
  • Semantic Kernel
  • LlamaIndex
  • CrewAI
  • AutoGen (preferred)

Backend Architecture

  • Design event-driven architectures.
  • Build high-performance APIs.
  • Develop resilient distributed systems.
  • Implement caching using Redis.
  • Build scalable workflow engines.
  • Integrate third-party enterprise APIs.

Database Development

Experience with:

Relational Databases

  • PostgreSQL
  • MySQL
  • Oracle

NoSQL Databases

  • MongoDB
  • DynamoDB
  • Cassandra

Vector Databases

  • Pinecone
  • ChromaDB
  • Weaviate
  • Milvus
  • PGVector

Cloud & DevOps

Develop and deploy applications using:

Cloud Platforms

  • AWS
  • Azure
  • Google Cloud Platform

Services

  • Kubernetes
  • Docker
  • OpenShift
  • ECS/EKS
  • Azure Kubernetes Service

CI/CD

  • Jenkins
  • GitHub Actions
  • GitLab CI
  • Azure DevOps

Infrastructure

  • Terraform
  • Helm
  • ArgoCD

Observability

Experience with:

  • Prometheus
  • Grafana
  • Datadog
  • ELK Stack
  • Splunk
  • OpenTelemetry

Required Technical Skills

Core Java

  • Java 17/21
  • Spring Boot
  • Spring MVC
  • Spring Data JPA
  • Spring Security
  • Spring Cloud
  • Hibernate
  • Maven/Gradle

API Development

  • REST APIs
  • GraphQL (preferred)
  • OpenAPI/Swagger
  • gRPC (nice to have)

Messaging

  • Apache Kafka
  • RabbitMQ
  • ActiveMQ

AI Technologies

  • Prompt Engineering
  • RAG
  • Embeddings
  • Vector Search
  • LLM APIs
  • AI Agents
  • AI Workflows
  • Semantic Search
  • Model Evaluation

AI Tools

  • GitHub Copilot
  • Cursor AI
  • Windsurf
  • Claude Code
  • ChatGPT Enterprise
  • OpenAI SDKs
  • Azure AI Studio
  • Amazon Bedrock

Security

  • OAuth2
  • JWT
  • SAML
  • API Security
  • OWASP Best Practices

Required Qualifications

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
  • 7+ years of Java backend development experience.
  • 2+ years of hands-on experience building AI-enabled applications.
  • Strong experience in developing enterprise microservices.
  • Experience integrating LLM APIs into production applications.
  • Strong understanding of distributed systems and cloud-native architecture.
  • Experience working in Agile/Scrum environments.
  • Excellent problem-solving and debugging skills.

Preferred Qualifications

  • Experience with LangGraph or agentic AI workflows.
  • Experience implementing RAG architectures at scale.
  • Experience with MCP (Model Context Protocol) integration.
  • Familiarity with AI evaluation frameworks.
  • Knowledge of AI guardrails, safety, and responsible AI practices.
  • Experience with multimodal AI models (text, image, audio).
  • Experience building AI copilots for enterprise applications.
  • Cloud certifications (AWS, Azure, or Google Cloud Platform).
  • Kubernetes certification is a plus.

Nice-to-Have Skills

  • Python for AI/ML integration
  • Neo4j or Knowledge Graphs
  • Apache Spark
  • Airflow
  • Elasticsearch/OpenSearch
  • Redis
  • Temporal.io
  • Camunda
  • Event Sourcing
  • CQRS Architecture

Soft Skills

  • Strong communication and collaboration skills.
  • Ability to translate business requirements into scalable technical solutions.
  • Strong analytical and troubleshooting abilities.
  • Passion for learning emerging AI technologies.
  • Experience mentoring junior developers and conducting code reviews.

What You'll Build

  • AI-powered enterprise applications
  • Intelligent document processing systems
  • AI chatbots and virtual assistants
  • Retrieval-Augmented Generation (RAG) platforms
  • AI copilots for internal business users
  • Semantic search and knowledge management systems
  • Scalable backend APIs supporting Generative AI applications
  • Multi-agent AI workflows integrated with enterprise platforms