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Ai Rag Jobs in Dallas, TX (NOW HIRING)

NTT DATA Services

AI Architect

Dallas, TX · On-site

It requires deep expertise in RAG (Retrieval-Augmented Generation) and Agentic AI architecture on cloud-native platforms, enabling intelligent, scalable, and production-ready AI systems after ...

Natsoft Corporation

Sierra.ai Consultant

Dallas, TX · On-site

... RAG) pipelines, and Agent SDKs - Skilled in building and deploying AI/LLM systems in production environments - Familiarity with AI agents, including evaluation frameworks, agent tooling, RAG ...

Programmers.io

Senior Generative AI Developer

Irving, TX · On-site

$116K - $157K/yr

Architect and develop Generative AI applications using RAG frameworks for enterprise-scale solutions. Design and implement robust system architectures for AI-driven platforms ensuring scalability ...

Futran Tech Solutions Pvt. Ltd.

Lead AI/ML Engineer

Dallas, TX · On-site

$94K - $124K/yr

Architect and develop Generative AI applications using RAG frameworks for enterprise-scale solutions. * Design and implement robust system architectures for AI-driven platforms ensuring scalability ...

Tanisha Systems

Sr. Generative AI Developer

Dallas, TX · On-site

$120K - $161K/yr

Key Responsibilities Architect and develop Generative AI applications using RAG frameworks for enterprise-scale solutions. Design and implement robust system architectures for AI-driven platforms ...

RealPage, Inc.

AI Engineer III

Richardson, TX · On-site +1

$55 - $73.75/hr

Reusable RAG pipelines and ingestion frameworks * Common UI components and design patterns for AI copilots and agents * Modular reusable coding practices for agentic back-end processes * Establish ...

BIGDATAAPPTECH LLC

AI Engineer

Irving, TX · On-site

Implement Retrieval-Augmented Generation (RAG) pipelines leveraging vector databases and embedding models. Create AI extensions, custom tools, and function-calling capabilities for intelligent ...

MSCCN

Lead Security Engineer - Red Team - Plano, TX

Plano, TX · On-site

$95K - $126K/yr

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

Upbound

AI Cybersecurity Engineer

Plano, TX

AI Cybersecurity Engineer Who We Are At Upbound Group, we are committed to elevating financial ... Harden RAG pipelines against retrieval manipulation attacks, indirect prompt injection via poisoned ...

Pereview Software

AI Engineer

Dallas, TX · On-site

Ready to Apply? Pereview Software is seeking an AI Engineer to join our growing Product and ... Develop andmaintainRetrieval-Augmented Generation (RAG)architecturesusing vector databases and ...

Upbound

AI Cybersecurity Engineer

Plano, TX

AI Cybersecurity Engineer Who We Are At Upbound Group, we are committed to elevating financial ... Harden RAG pipelines against retrieval manipulation attacks, indirect prompt injection via poisoned ...

RealPage, Inc.

Agentic AI Developer III

Richardson, TX · On-site +1

Reusable RAG pipelines and ingestion frameworks * Common UI components and design patterns for AI copilots and agents * Modular reusable coding practices for agentic back-end processes * Establish ...

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Ai Rag information

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$31.7K

$57.6K

$82.6K

How much do ai rag jobs pay per year?

As of Jul 20, 2026, the average yearly pay for ai rag in Dallas, TX is $57,618.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,500.00 and $64,300.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

Which AI is best at RAG?

For an AI Rag role, the best AI systems for Retrieval-Augmented Generation (RAG) tasks typically include models like OpenAI's GPT-4, Google's Bard, and Meta's Llama 2, which are capable of integrating retrieval components with language generation. Success in RAG depends on the model's ability to efficiently access and incorporate external data, as well as the implementation of effective retrieval mechanisms and fine-tuning. Skills in natural language processing, knowledge of retrieval systems, and experience with relevant tools are essential for this role.

What engineer makes 500,000 a year?

Senior software engineers, especially those working in high-demand fields like artificial intelligence or machine learning at large tech companies, can earn $500,000 or more annually. Compensation often includes base salary, bonuses, and stock options, and requires advanced skills, extensive experience, and often a master's or Ph.D. in a related field.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer, AI research director, or executive roles like AI CTO. These roles often require advanced skills in data science, deep learning, and experience with tools like TensorFlow or PyTorch, along with a strong track record of innovation and leadership in the field.

What are AI RAGs?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

Which 3 jobs will survive AI?

AI Rag is a role that involves managing and interpreting AI outputs, and jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to survive AI automation. Examples include healthcare professionals, skilled tradespeople, and roles in education. These jobs often require human judgment, interpersonal skills, and adaptability that AI cannot fully replicate.

What are some common challenges faced by AI RAG (Retrieval-Augmented Generation) engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.
What are popular job titles related to Ai Rag jobs in Dallas, TX? For Ai Rag jobs in Dallas, TX, the most frequently searched job titles are:
What job categories do people searching Ai Rag jobs in Dallas, TX look for? The top searched job categories for Ai Rag jobs in Dallas, TX are:
What cities near Dallas, TX are hiring for Ai Rag jobs? Cities near Dallas, TX with the most Ai Rag job openings:
Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Dallas, TX as of July 2026, with employment types broken down into 80% Full Time, 18% Part Time, and 2% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $57,618 per year, or $27.7 per hour.
AI Architect

AI Architect

NTT DATA Services

Dallas, TX • On-site

Full-time

Re-posted 6 days ago

NTT Data rating

7.4

Company rating: 7.4 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

107th of 210 rated it services

Job description

Req ID: 375661
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking a AI Architect to join our team in Dallas, Texas (US-TX), United States (US).
Job Title: AI Architect
Experience level: 10 + years
Job Summary
We are seeking an experienced AI Architect to design and lead enterprise-scale AI, ML, and Generative AI solutions built on AWS and Azure as the core AI foundation, with Microsoft Copilot as the primary user experience layer. The role is responsible for designing the end-to-end AI solution architecture, ensuring alignment with enterprise systems, scalability, and governance standards while integrating AI into the broader IT landscape. It requires deep expertise in RAG (Retrieval-Augmented Generation) and Agentic AI architecture on cloud-native platforms, enabling intelligent, scalable, and production-ready AI systems after understanding the current product architecture. The candidate should also be able to conduct POCs to demonstrate proof of design considerations.
Platform & Enablement Roles
  • AI Platform Admin (M365, copilot Studio) Manages AI platforms and environments, including access provisioning, governance controls, and policy enforcement (e.g., DLP, security, and compliance).
  • AI Reusable Utility Develops reusable components (e.g., prompts, connectors, APIs, templates) to accelerate AI solution delivery and promote standardization across use cases.
  • AI Common Infrastructure, Framework & Observability Architect (AWS and Azure) Designs and maintains the foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, and governed AI operations.

Core Responsibilities
  1. Architectural Design: Define the end-to-end blueprints spanning data ingestion, model training, inference, and continuous monitoring. design end-to-end artificial intelligence solutions ensuring models scale efficiently align with enterprise systems and meet governance standards. They act as the vital bridge linking theoretical AI models built by data scientists with production-ready, secure applications integrated into the broader IT landscape.
  1. Enterprise Integration: Seamlessly embed AI/ML features and multi-agent workflows into legacy applications, ERPs, and cloud-native systems.
  2. Governance & Compliance: Implement ethical AI guardrails, model risk management, data privacy protections and explainability standards.
  3. Scalability & MLOps: Establish CI/CD for AI, model versioning, automated retraining, and drift detection to prevent performance degradation.
  4. Tech Stack Strategy: Make crucial "build vs. buy" decisions for infrastructure, weighing tradeoffs of on-premises, hybrid, and cloud environments.
  5. Leadership & Collaboration:
    • Serve as a technical thought leader for AI, GenAI, and data platforms.
    • Mentor data scientists, ML engineers, and data engineers.
    • Collaborate with business and product teams to translate requirements into AI-driven solutions.
    • Evaluate emerging AI technologies and guide strategic adoption.
  1. AI, ML & GenAI Architecture
    • Design and define end-to-end AI solution architectures covering data ingestion, model training, deployment, monitoring, and governance, ensuring alignment with enterprise systems and IT landscape while meeting scalability and governance standards.
    • Design scalable, cloud-native AI platforms on AWS and Azure.
    • Architect solutions for both batch and real-time inference workloads.
  1. RAG (Retrieval-Augmented Generation)
    • Architect and implement RAG pipelines using structured and unstructured enterprise data.
    • Design ingestion, chunking, embedding, and retrieval strategies for RAG systems.
    • Integrate vector databases (e.g., Pinecone, FAISS, Milvus, Azure AI Search, Amazon OpenSearch).
    • Ensure relevance, freshness, observability, and security of RAG-based AI systems.
  2. Agentic AI & Autonomous Systems
    • Design Agentic AI architecture enabling autonomous decision-making and task execution.
    • Orchestrate multi-agent systems using tools, memory, and reasoning workflows.
    • Implement guardrails, human-in-the-loop controls, and observability for agent-based systems.
    • Enable enterprise use cases such as AI assistants, Microsoft Copilot-integrated workflows, task automation, and decision intelligence.
  1. MLOps & LLMOps
    • Define and implement MLOps / LLMOps frameworks for CI/CD, versioning, monitoring, and drift detection.
    • Enable experimentation, evaluation, and governance of ML models and LLM-based systems.
    • Ensure compliance with security, privacy, and responsible AI guidelines.
  2. Cloud & Platform Engineering
    • Architect AI solutions on AWS and Azure as the primary cloud platforms, integrating Microsoft Copilot as the enterprise user experience layer.
    • Integrate AI platforms with enterprise applications, APIs, and data sources.
    • Design highly available, secure, and scalable AI systems.

Required Skills
  • Engineering Foundation: 7+ years of deep knowledge of MLOps, containerization (Docker/Kubernetes), and CI/CD pipelines.
  • Cloud Platforms: 5+ years of advanced expertise in deploying on major hyperscalers like AWS Machine Learning, Azure AI, or Google Vertex AI.
  • Data Management: 5+ years of Proficiency in designing feature stores, vector databases, and real-time/batch data pipelines.
  • AI/ML Frameworks: 3 to 5 years of familiarity with concepts like Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), and frameworks like PyTorch or TensorFlow.

#LI-NorthAmerica
About NTT DATA
NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.
Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client's needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, https://us.nttdata.com/en/contact-us.
NTT DATA endeavors to make https://us.nttdata.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at https://us.nttdata.com/en/contact-us. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here. If you'd like more information on your EEO rights under the law, please click here. For Pay Transparency information, please click here.

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About NTT DATA

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NTT DATA Services is a global business and IT services provider specializing in digital, cloud and automation across a comprehensive portfolio of consulting, applications, infrastructure and business process services. We are part of the NTT family of companies, a partner to 85 % of the Fortune 100.

Industry

It services

Company size

10,000+ Employees

Headquarters location

Plano, TX, US

Year founded

1967

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