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Large Language Model Llm Jobs (NOW HIRING)

LLM Infrastructure Engineer

Houston, TX · On-site

$97K - $127K/yr

We are looking for a Senior Python / AI API Engineer to build and deploy production-grade services powering Large Language Model (LLM) applications. This role focuses on developing high-performance ...

The LLM Engineer serves as the organization's AI technical lead responsible for designing, implementing, and optimizing Large Language Model (LLM) solutions that automate business processes, improve ...

LLM Engineer

Northbrook, IL · On-site

$85K - $115K/yr

The LLM Engineer serves as the organization's AI technical lead responsible for designing, implementing, and optimizing Large Language Model (LLM) solutions that automate business processes, improve ...

AI Architect

OR · Remote

Artificial and Large Language Model Architect As an AI & LLM Architect , you will play a pivotal role in designing and implementing the technology architecture for advanced AI (including Large ...

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Large Language Model Llm information

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

As of Jul 14, 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) job?

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.

Which 3 jobs will survive AI?

Large Language Model (LLM) specialists, healthcare professionals, and skilled tradespeople are likely to continue thriving as AI automates routine tasks. These roles require complex decision-making, emotional intelligence, or manual skills that are difficult for AI to replicate fully. Continuous learning and adaptability remain important for job security in these fields.

What jobs can I do with LLM?

Large Language Models (LLMs) are used in roles such as AI research scientist, NLP engineer, data scientist, and machine learning engineer. These jobs involve developing, fine-tuning, and deploying LLMs, often requiring skills in programming, data analysis, and understanding of AI frameworks like TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive in the Large Language Model Llm position, and why are they important?

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 pay 500,000 a year?

High-paying jobs that can reach or exceed $500,000 annually include executive roles such as CEOs, CFOs, and other C-suite positions, as well as specialized professions like top-tier surgeons, investment bankers, and successful entrepreneurs. These roles typically require extensive experience, advanced skills, and often involve leadership, risk management, or highly specialized expertise.

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level positions in artificial intelligence, such as senior machine learning engineers, AI research directors, or chief AI officers, often requiring advanced skills in deep learning, data science, and programming. These roles usually involve leadership, strategic planning, and extensive experience, and they may be found in large tech companies or specialized AI firms. Compensation at this level reflects significant expertise and responsibility in developing and deploying AI systems.
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Large Language Model (LLM) AI Engineer

Large Language Model (LLM) AI Engineer

Oran, Inc.

Herndon, VA • On-site, Remote

Full-time

Re-posted 24 days ago


Job description

Experience Required
7+ Years Overall | 3+ Years in Generative AI / LLMs
Position Overview
We are seeking a Large Language Model (LLM) AI Engineer to design, fine-tune, evaluate, and integrate generative AI and LLM-based solutions in healthcare, scientific, and regulated environments. The ideal candidate will possess expertise in modern AI architectures, vector databases, prompt engineering, and AI governance.
Key Responsibilities
  • Design and implement generative AI and LLM solutions.
  • Fine-tune and evaluate foundation models.
  • Develop AI workflows using agentic AI frameworks.
  • Build RAG architectures and vector database integrations.
  • Develop APIs and cloud-native AI solutions.
  • Implement hallucination mitigation and AI governance controls.