1

Rag Engineer Jobs in Texas (NOW HIRING)

Looking for Java/Python Developers who also has experience with GenAI concepts (LLM, RAG, Vectors, etc). This resource will help a team that is Java heavy to solve some use cases possibly integrating ...

New

AI/ML Engineer

Plano, TX · On-site

$120 - $160/hr

Hands-on with prompt design, evaluation, LLM orchestration, and RAG implementation patterns ... AWS Solutions Architect, AWS DevOps Engineer, or equivalent industry certifications.

Looking for Java/Python Developers who also has experience with GenAI concepts (LLM, RAG, Vectors, etc). This resource will help a team that is Java heavy to solve some use cases possibly integrating ...

New

Databricks Data Engineer

Austin, TX · On-site

$113K - $136K/yr

Design and deploy Generative AI and RAG based solutions using Databricks Mosaic AI and Vector ... Mentor data engineers, analysts, and architects on Databricks technologies and platform adoption.

AI Engineer

Austin, TX · On-site

$100K - $130K/yr

... loops, RAG pipelines, and tool-calling architectures - Experience designing multi-agent systems and integrating LLMs with internal/external APIs Added Advantage: AI platform/developer tooling ...

AI/ML Software Engineer

Plano, TX · On-site

$55 - $60/hr

Implement RAG solutions using chunking, embeddings, vector databases, and retrieval techniques ... Apply prompt engineering techniques including system prompts, few-shot prompting, tool calling, and ...

AI Engineer

Dallas, TX · On-site

$90 - $120/hr

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and ... Collaborate with product managers, engineers, and business stakeholders to identify opportunities ...

AI Engineer

Irving, TX · On-site

$120K - $130K/yr

RAG; autonomous decision frameworks; Python/R/SQL/SAS; vector databases; semantic search; knowledge graphs; metadata management; production ML/AI deployment, monitoring, governance, explainability ...

Sr AI Agentic Engineer

Spring, TX · On-site

$93K - $127K/yr

Senior AI Agentic Engineer Spring TX - 3 days in office (Tues/Wed/Thurs) Duration: Full time ... Design and optimize RAG pipelines including document ingestion, chunking strategies, embedding ...

AI Software Engineer Work Model: Dallas, TX | Austin, TX | San Francisco, CA (Onsite-4 Days ... Develop and optimize Retrieval-Augmented Generation (RAG) systems * Build AI agents, workflow ...

GenAI & Agentic AI Engineer

Dallas, TX · On-site

$120 - $160/hr

GenAI & Agentic AI Engineer Location: Whippany, NJ (Hybrid) Hire Type: FTE Must be legally ... Design and implement RAG pipelines , vector search solutions, and embedding‑based retrieval ...

AI/ML Engineer

Plano, TX · On-site

$60/hr

Solid knowledge of Prompt engineering/context engineering, Long-term vs short-term memory, Token management, RAG & Vectorization, Cache management, different frameworks of Agentic AI, including. Nice ...

AI Engineer

Plano, TX · On-site

$78/hr

AI Engineer Location: Plano, TX Duration: 6 Months + Extension Bill Rate: $78/hour Job Type: C2C ... Build LLM-powered applications, RAG pipelines, AI agents, and intelligent automation solutions

AI Cybersecurity Engineer

Plano, TX · On-site

$120 - $140/hr

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 ...

Build and deploy intelligent agents with advanced capabilities including prompt engineering, context engineering, and retrieval-augmented generation (RAG) pipelines * Implement tool use orchestration ...

Showing results 41-60

Rag Engineer information

See Texas salary details

$55.4K

$84.3K

$143K

How much do rag engineer jobs pay per year?

As of Sep 4, 2026, the average yearly pay for rag engineer in Texas is $84,325.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,800.00 and $97,800.00 per year, depending on experience, location, and employer.

What is the difference between Rag Engineer vs Textile Technician?

AspectRag EngineerTextile Technician
Required CredentialsEngineering degree, technical certificationsDiploma or degree in textiles or related field
Work EnvironmentFactories, manufacturing plants, R&D labsTextile mills, production facilities, quality control labs
Industry UsageDesigning and improving rag production processesMonitoring textile quality, testing fabrics

While both roles involve working within the textile industry, a Rag Engineer primarily focuses on the engineering aspects of rag production, process optimization, and machinery, whereas a Textile Technician concentrates on fabric testing, quality control, and ensuring textile standards are met. The roles often overlap in industry settings but differ in technical focus and responsibilities.

How to become a rag engineer?

To become a rag engineer, you typically need a bachelor's degree in engineering, materials science, or a related field. Relevant skills include knowledge of manufacturing processes, quality control, and proficiency with industry tools and equipment; certifications in quality management or safety can also be beneficial. Gaining experience through internships or entry-level positions in manufacturing environments is important for career advancement.

What are popular job titles related to Rag Engineer jobs in Texas?

For Rag Engineer jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Rag Engineer jobs in Texas look for?

The top searched job categories for Rag Engineer jobs in Texas are:

What cities in Texas are hiring for Rag Engineer jobs?

Cities in Texas with the most Rag Engineer job openings:

Infographic showing various Rag Engineer job openings in Texas as of August 2026, with employment types broken down into 90% Full Time, 6% Part Time, and 4% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $84,325 per year, or $40.5 per hour.

Senior Generative AI Developer

Programmers.io

Irving, TX • On-site

$116K - $157K/yr

Full-time

Re-posted 24 days ago


Job description

Job Description:
We are seeking an experienced Senior Generative AI Developer to design and implement cutting-edge AI solutions leveraging Retrieval-Augmented Generation (RAG) techniques. The ideal candidate will have strong expertise in Python programming, FastAPI, and cloud platforms (AWS, Azure, or GCP). This role requires a deep understanding of system architecture design, scalable APIs, and end-to-end AI solution development.

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 ensuring scalability, security, and performance.
Build and optimize APIs using FastAPI for seamless integration with AI models and data pipelines.
Collaborate with cross-functional teams to integrate AI solutions into existing systems and workflows.
Implement data ingestion, preprocessing, and retrieval mechanisms for large-scale knowledge bases.
Ensure compliance with best practices for cloud deployment (AWS, Azure, or GCP).
Conduct performance tuning and optimization of AI models and APIs.
Stay updated with the latest advancements in Generative AI, LLMs, and RAG methodologies.

Required Skills & Qualifications
8+ years of professional experience in software development and system design.
Strong proficiency in Python and experience with FastAPI for API development.
Hands-on experience with Generative AI frameworks and RAG architectures.
Solid understanding of system and architecture design principles for distributed applications.
Experience deploying solutions on any major cloud platform (AWS, Azure, GCP).
Familiarity with vector databases, embedding models, and retrieval pipelines.
Strong problem-solving skills and ability to work in a fast-paced environment.

Preferred Qualifications
Experience with LLM fine-tuning, prompt engineering, and model evaluation.
Knowledge of containerization (Docker) and orchestration (Kubernetes).
Exposure to CI/CD pipelines and DevOps practices.