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Large Language Model Llm Jobs (NOW HIRING)

Xforia, Inc.

Data Scientist

... Large Language Model (LLM) solution to check coherence and consistency. Resource will also develop and modify existing models related to customer long term engagement and retention. This role will ...

Hippocratic AI

LLM Inference Engineer

Menlo Park, CA · On-site

About the Role We're seeking an experienced LLM Inference Engineer to optimize our large language model (LLM) serving infrastructure. The ideal candidate has: * Extensive hands-on experience with ...

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How much do large language model llm jobs pay per hour?

As of Aug 11, 2026, the average hourly pay for large language model llm in the United States is $24.34, according to ZipRecruiter salary data. Most workers in this role earn between $18.99 and $29.09 per hour, depending on experience, location, and employer.

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.
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Large Language Model Llm jobs near you
Infographic showing various Large Language Model Llm job openings in the United States as of August 2026, with employment types broken down into 56% Full Time, 6% Part Time, and 38% Contract. Highlights an 69% In-person, 6% Hybrid, and 25% Remote job distribution, with an average salary of $50,625 per year, or $24.3 per hour.

LLM Engineer (GCP Preferred)

Krest Global Solutions

Atlanta, GA • Hybrid

$58 - $60/hr

Contractor

Re-posted 14 days ago

Job description

Job Title: LLM Engineer (GCP Preferred)

Work Time Zone: EST

Rate: $60/hour on 1099/C2C

Location: Atlanta, GA (Hybrid – 3 days on-site)

We are seeking a highly skilled and motivated LLM Engineer to design, build, and deploy advanced large language model (LLM) solutions that enhance procurement workflows and drive business automation. The ideal candidate will have a strong background in natural language processing, deep learning, and AI agent design, with hands-on experience fine-tuning foundation models and deploying them on Google Cloud Platform (GCP).

Key Responsibilities:

  • AI Agent Development
    Design and implement LLM-powered AI agents that optimize and automate procurement-related tasks, ensuring reliability, explainability, and business alignment.
  • Model Fine-Tuning & Optimization
    Fine-tune foundation models for domain-specific tasks, focusing on accuracy, latency, and scalability. Apply techniques such as parameter-efficient fine-tuning, prompt tuning, and adapter training.
  • Pipeline Engineering
    Build and maintain robust, production-grade pipelines for data ingestion, model training, evaluation, and inference using GCP services and open-source tools.
  • Prompt Engineering & RAG Implementation
    Leverage prompt engineering and Retrieval-Augmented Generation (RAG) to improve contextual accuracy and relevance of model outputs.
  • Stakeholder Collaboration
    Work closely with procurement experts, data engineers, and business leaders to gather requirements, align goals, and deliver impactful AI solutions.
  • Model Evaluation & Monitoring
    Establish evaluation metrics and monitoring tools to track model performance, accuracy, bias, and drift in real-world applications.
  • Integration & Deployment
    Collaborate with cross-functional teams to integrate LLMs into existing systems, leveraging LangChain, LangGraph, and GCP AI tools like Vertex AI for seamless deployment.

Must-Have Qualifications:

  • Master’s degree in mathematics, Physics, Computer Science,
  • 7 – 10 + years of experience in NLP, LLM development, or AI-driven automation.
  • Expertise in Python and deep learning frameworks such as PyTorch and TensorFlow.
  • Proficiency with LangChain, LangGraph, Hugging Face Transformers, and LLM model hubs.
  • Experience fine-tuning large-scale models and optimizing for real-time inference.
  • Solid understanding of vector databases, knowledge graphs, and embedding techniques.
  • Strong communication skills with the ability to translate complex AI concepts to non-technical stakeholders.
  • Proven experience working with Google Cloud Platform (GCP), especially with services like Vertex AI, BigQuery, and Cloud Functions.
  • Familiarity with multi-agent systems and reinforcement learning is a strong plus.