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Large Language Model Llm Jobs in Texas (NOW HIRING)

Vizient

AI & Decision Intelligence Director

Irving, TX

$135K - $236K/yr

Build agentic AI and large language model (LLM)-powered applications and workflows that enhance productivity and insight generation. * Apply advanced analytics techniques to uncover opportunities and ...

Vizient

Sr AI/ML Engineer

Irving, TX

$102K - $179K/yr

You will lead the full AI lifecycle from experimentation through production, developing both traditional ML models and Large Language Model (LLM)-powered systems such as Retrieval-Augmented ...

Vizient, Inc.

Sr AI/ML Engineer

Irving, TX · On-site

$102K - $179K/yr

You will lead the full AI lifecycle from experimentation through production, developing both traditional ML models and Large Language Model (LLM)-powered systems such as Retrieval-Augmented ...

Vizient, Inc.

Sr AI/ML Engineer

Irving, TX · On-site

$102K - $179K/yr

You will lead the full AI lifecycle from experimentation through production, developing both traditional ML models and Large Language Model (LLM)-powered systems such as Retrieval-Augmented ...

Solera

Lead Software Engineer

Westlake, TX · On-site

LLM Skills: Hands-on experience building AI agents with Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG), as well as tuning models. * LLM Model : Apt with GPTs, Llama, or ...

Diablo Convoy

LLM Engineer

Houston, TX · On-site

$120K - $130K/yr

We are seeking a detail-oriented LLM Automation Engineer to support AI-driven data analysis ... Use AI tools and large language models (LLMs) to summarize, classify, and analyze unstructured ...

Solera

Lead Software Engineer

Westlake, TX · On-site

LLM Skills: Hands-on experience building AI agents with Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG), as well as tuning models. * LLM Model : Apt with GPTs, Llama, or ...

SpangleAI

AI/ML Scientist

Austin, TX · On-site

... Large Language Models (e.g., GPT, BERT, T5, LLaMA) • Experience with LLM fine-tuning, RL post training, prompt engineering, and deploying LLMs for applications such as natural language ...

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Large Language Model Llm information

What are some common challenges faced by large language model llm engineers in their day-to-day work?

LLM Engineers often encounter challenges related to scaling models efficiently, optimizing performance on large and complex datasets, and ensuring the responsible use of AI technologies. Balancing the trade-offs between model accuracy, speed, and ethical considerations can be demanding, especially as real-world applications often require rapid iterations and rigorous testing. Additionally, staying updated with the latest research advancements and integrating new methods into production systems is an ongoing responsibility. Many engineers tackle these challenges by working closely with data scientists, researchers, and product teams in collaborative, agile environments.

What is a large language model llm?

A Large Language Model (LLM) job typically involves working with advanced AI models designed to understand and generate human-like text. Roles in this field may include research, data engineering, model fine-tuning, prompt engineering, or application development. Professionals in LLM jobs often work with machine learning algorithms, natural language processing (NLP), and large-scale datasets to enhance AI capabilities. These roles are common in AI-driven industries, including tech companies, research institutions, and startups. Strong programming skills, knowledge of deep learning frameworks, and expertise in NLP are often required.

What are the key skills and qualifications needed to thrive in the large language model llm position?

Excelling in the role of a Large Language Model (LLM) Engineer requires strong expertise in natural language processing, machine learning, and computer programming, often supported by an advanced degree in computer science or a related field. Familiarity with industry-standard frameworks like PyTorch or TensorFlow, as well as experience with cloud computing platforms and large-scale data management, is highly valued. Communication, creativity, and problem-solving are essential soft skills to effectively collaborate with cross-functional teams and innovate solutions. These skills ensure the development, deployment, and refinement of powerful language models that can address diverse business needs and technical challenges.

What jobs can I do with a large language model?

A large language model can be used in roles such as AI content developer, chatbot designer, or natural language processing specialist. These jobs involve tasks like training, fine-tuning models, creating AI-driven applications, and improving language understanding, often requiring skills in programming, data analysis, and machine learning tools.
What are the most commonly searched types of Large Language Model Llm jobs in Texas? The most popular types of Large Language Model Llm jobs in Texas are:
What are popular job titles related to Large Language Model Llm jobs in Texas? For Large Language Model Llm jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Large Language Model Llm jobs in Texas look for? The top searched job categories for Large Language Model Llm jobs in Texas are:
Large Language Model Llm jobs near you
Infographic showing various Large Language Model Llm job openings in Texas as of August 2026, with employment types broken down into 64% Full Time, 6% Part Time, and 30% Contract. Highlights an 75% In-person, 5% Hybrid, and 20% Remote job distribution.

W2 AI Engineer (NO C2C)

Inabia Solutions and Consulting, Inc.

Grand Prairie, TX • On-site

Full-time

Posted 7 days ago

Job description

Overview
Inabia is seeking an AI Engineer to design, build, and deploy advanced large language model solutions and retrieval-augmented generation (RAG) pipelines integrated into enterprise cloud environments. This role demands deep hands-on expertise in leading LLM frameworks alongside the communication skills necessary to translate complex model outputs into clear, actionable recommendations for operations and executive stakeholders. Candidates with manufacturing domain experience are strongly encouraged to apply.
Responsibilities
  • Architect end-to-end RAG pipelines leveraging Hugging Face, LangChain, and OpenAI API to solve enterprise-scale challenges.
  • Integrate generative AI capabilities into existing enterprise cloud environments and data infrastructure.
  • Develop, fine-tune, and evaluate large language models for domain-specific use cases.
  • Write production-quality code in Python, R, and SQL to support data ingestion, model serving, and analytics workflows.
  • Translate complex model outputs and analytical findings into clear, actionable recommendations for operational and executive stakeholders.
  • Collaborate cross-functionally with engineering, operations, and leadership teams to identify and prioritize high-impact AI use cases.
  • Monitor model performance, troubleshoot issues, and continuously improve pipeline reliability and accuracy.
  • Document architectures, methodologies, and outcomes to support knowledge transfer and reproducibility.

Key Qualifications
  • 5+ years of experience as an AI/ML Engineer (exceptional candidates with more experience are equally welcome).
  • Hands-on proficiency with LLM/AI frameworks: Hugging Face, LangChain, and OpenAI API.
  • Demonstrated experience architecting and deploying RAG pipelines in production environments.
  • Strong programming skills in Python, R, and SQL.
  • Proven ability to communicate technical concepts and model results to non-technical operations and executive audiences.
  • Experience integrating AI solutions into enterprise cloud platforms.

Preferred Qualifications
  • 2-4 years of experience working in a manufacturing domain.
  • Familiarity with shop floor operations, production planning, and systems such as MES, SCADA, and ERP.
  • Proficiency in industrial communication protocols (OPC-UA, MQTT, Modbus) with demonstrated ability to bridge OT/IT systems for real-time data extraction.
  • Applied experience with OEE, Six Sigma, SPC, and lean manufacturing methodologies to drive measurable improvements in yield, uptime, and operational efficiency.

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