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Retrieval Augmented Generation Jobs in Houston, TX

Marencor

Gen AI/ML Solution Architect

Houston, TX · On-site

$60.25 - $79.25/hr

Develop Retrieval-Augmented Generation (RAG) pipelines for intelligent document retrieval and question-answering systems. * Implement personalized recommendation engines using cutting-edge frameworks ...

NAVA Software Solutions

Sr AI Agentic Engineer

Spring, TX · On-site

$93K - $127K/yr

Retrieval-Augmented Generation (RAG) * Design and optimize RAG pipelines including document ingestion, chunking strategies, embedding models, vector store selection, and retrieval ranking for ...

W3Global

Senior Data Scientist

Houston, TX · On-site

Build and deploy RAG (Retrieval-Augmented Generation) systems & AI chat interfaces Work closely with client data science teams (ML/DL ecosystems) Develop GenAI-based enterprise knowledge solutions ...

NobleAI

Lead Machine Learning Engineer

Houston, TX · On-site +1

$97K - $128K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks, guardrail and assessment mechanisms for end to end lifecycle for customized models. * Collaborate ...

NobleAI

Lead Machine Learning Engineer

Houston, TX · Remote

$104K - $138K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks, guardrail and assessment mechanisms for end to end lifecycle for customized models. * Collaborate ...

NobleAI

Lead Machine Learning Engineer

Houston, TX · On-site +1

$97K - $128K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks, guardrail and assessment mechanisms for end to end lifecycle for customized models. * Collaborate ...

Jobtailor

AI Solutions Architect

Houston, TX · On-site

$120 - $180/hr

Practical experience with Generative AI and Retrieval-Augmented Generation, or RAG * Strong technical background with C#, .NET, React.js, and Node.js * Experience with cloud infrastructure ...

Slalom Consulting

General Information

Houston, TX · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Build advanced retrieval-augmented generation (RAG) systems including vector databases, embedding strategies, chunking optimization, hybrid search, re-ranking, and multi-source data synthesis

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All JobsRetrieval Augmented Generation JobsRetrieval Augmented Generation Jobs in Houston, TX

Retrieval Augmented Generation information

What does a retrieval augmented generation engineer do?

A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.

What is a retrieval augmented generation?

A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.

What skills and qualifications are needed for retrieval augmented generation?

To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.

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For Retrieval Augmented Generation jobs in Houston, TX, the most frequently searched job titles are:

What job categories do people searching Retrieval Augmented Generation jobs in Houston, TX look for?

The top searched job categories for Retrieval Augmented Generation jobs in Houston, TX are:

What cities near Houston, TX are hiring for Retrieval Augmented Generation jobs?

Cities near Houston, TX with the most Retrieval Augmented Generation job openings:

Retrieval Augmented Generation jobs near you
Infographic showing various Retrieval Augmented Generation job openings in Houston, TX as of August 2026, with employment types broken down into 46% Full Time, and 54% Contract. Highlights an 74% In-person, and 26% Remote job distribution.

Gen AI/ML Solution Architect

Marencor

Houston, TX • On-site

$60.25 - $79.25/hr

Contractor

Re-posted 4 days ago

Job description

Position: Gen AI/ML Solution Architect

Location: Houston, TX - (5 days onsite per week)
 

Notes:

  • Minimum 2-3 Project on GEN AI LLM Architect
  • Machine Learning with MLOps
  • Convert business problems to solutions

Job Summary:
We are seeking an experienced Gen AI/ML Solution Architect to lead the design, development, and deployment of advanced AI-driven solutions. The ideal candidate will have over a decade of expertise in AI, machine learning, data mining, NLP, and predictive analytics, with proven success in architecting large-scale data science solutions and delivering business impact.

 

Key Responsibilities:

  • Lead end-to-end Gen AI/ML solution architecture — from problem definition, data acquisition, and feature engineering to model deployment and monitoring.
  • Collaborate with business stakeholders to define requirements and translate them into scalable AI/ML solutions.
  • Design and implement advanced NLP systems using state-of-the-art models (BERT, ELMO, word2vec) for tasks such as sentiment analysis, named entity recognition, and topic modeling.
  • Build and integrate predictive models, leveraging cloud-based platforms such as Azure Machine Learning for forecasting and analytics.
  • Develop Retrieval-Augmented Generation (RAG) pipelines for intelligent document retrieval and question-answering systems.
  • Implement personalized recommendation engines using cutting-edge frameworks (e.g., Semantic Kernel).
  • Ensure solutions are optimized for performance, scalability, and maintainability in production environments.
  • Mentor and guide teams on AI/ML best practices, frameworks, and emerging technologies.

 

Required Qualifications:

  • Bachelor’s in Engineering or Computer Science and a Master’s in Information Technology, Telecommunications, or a related field.
  • 14+ years of professional experience in AI, data mining, deep learning, predictive analytics, and machine learning.
  • Proven track record in the full data science project lifecycle, including data wrangling, statistical analysis, and data visualization.
  • Proficiency in Python and R, with expertise in AI/ML libraries such as TensorFlow, PyTorch, Scikit-learn, Transformers, and visualization tools (Matplotlib, Seaborn, ggplot2).
  • Strong knowledge of NLP techniques and frameworks, vector databases, and MLOps workflows.
  • Experience with cloud-based AI platforms (Azure ML, AWS Sagemaker, or GCP AI Platform).
  • Solid understanding of cognitive AI systems, intelligent automation, and decision-support systems.

 

Preferred Skills:

  • Experience with Retrieval-Augmented Generation (RAG) pipelines and Semantic Kernel integration.
  • Expertise in deploying AI solutions at enterprise scale with robust API integrations.
  • Strong communication and leadership skills for cross-functional collaboration.