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Hourly Large Language Model Llm Jobs (NOW HIRING)

LLM Specialist

Columbia, MD · On-site

$104K - $145K/yr

The LLM Specialist will drive the design, development, and operationalization of advanced large-language-model capabilities across a cloud-based analytics ecosystem. This role leads innovation ...

LLM Specialist

Columbia, MD · On-site

$104K - $145K/yr

The LLM Specialist will drive the design, development, and operationalization of advanced large-language-model capabilities across a cloud-based analytics ecosystem. This role leads innovation ...

LLM Specialist

Columbia, MD · On-site +1

$104K - $145K/yr

The LLM Specialist will drive the design, development, and operationalization of advanced large-language-model capabilities across a cloud-based analytics ecosystem. This role leads innovation ...

LLM Engineer About the role As LLM Engineer , you will make an impact by designing, building, optimizing, and deploying Large Language Model (LLM) and Small Language Model (SLM) solutions that power ...

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Hourly Large Language Model Llm information

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

As of Sep 14, 2026, the average hourly pay for hourly 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 is an hourly large language model LLM?

Hourly Large Language Model (LLM) jobs are roles where individuals work with LLMs, such as ChatGPT or similar AI systems, on an hourly basis. These positions often involve tasks like data annotation, prompt engineering, AI model evaluation, or content generation. Workers may be responsible for improving AI responses, testing models, or creating training data. The 'hourly' aspect means they are paid based on the number of hours worked, rather than a fixed salary or per-project rate. Such jobs are common in tech companies, research organizations, or freelance platforms.

What are the key skills and qualifications needed to thrive as a large language model (LLM) engineer?

To thrive as a Large Language Model (LLM) Engineer, you need a solid background in machine learning, natural language processing, and programming—typically with a degree in computer science or a related field. Experience with frameworks like TensorFlow or PyTorch, familiarity with cloud platforms, and knowledge of model deployment tools are highly valued, along with certifications in AI or data science. Strong problem-solving skills, creativity, and effective communication help you collaborate with cross-functional teams and innovate solutions. These competencies are crucial for developing, optimizing, and scaling LLMs to meet evolving business and research needs.

What are some common challenges faced by hourly large language model (LLM) annotators and how can they be addressed?

Hourly LLM annotators often face challenges such as maintaining consistency in labeling, handling ambiguous or unclear data, and managing the repetitive nature of annotation tasks. To address these challenges, it's helpful to regularly review annotation guidelines, participate in team discussions to clarify uncertainties, and leverage available feedback from quality assurance checks. Collaborating with teammates and project managers can also provide support and ensure alignment on task expectations, making the work environment more collaborative and improving overall accuracy.

What is the difference between Hourly Large Language Model Llm vs Data Scientist?

AspectHourly Large Language Model LlmData Scientist
Required CredentialsKnowledge of AI, NLP, programming skillsDegree in Data Science, Statistics, or related field
Work EnvironmentTech companies, AI research labs, freelance projectsCorporate, consulting firms, research institutions
Industry UsageDeveloping and fine-tuning language models, AI applicationsData analysis, predictive modeling, data visualization

While both roles involve working with data and advanced technology, Hourly Large Language Model Llm focuses on developing and deploying AI language models, whereas Data Scientists analyze data to inform business decisions. The roles share skills in programming and data handling but differ in their primary objectives and work environments.

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Infographic showing various Hourly Large Language Model Llm job openings in the United States as of September 2026, with employment types broken down into 100% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $50,625 per year, or $24.3 per hour.

LLM Specialist

Columbia, MD • On-site

eSimplicity
IT Services • 51 - 200 employees

$104K - $145K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 24 days ago


Job description

Description:

About Us:

eSimplicity is a modern digital services company that partners with government agencies to improve the lives and protect the well-being of all Americans, from veterans and service members to children, families, and seniors. Our engineers, designers, and strategists cut through complexity to create intuitive products and services that equip federal agencies with solutions to courageously transform today for a better tomorrow.


Responsibilities:

The LLM Specialist will drive the design, development, and operationalization of advanced large-language-model capabilities across a cloud-based analytics ecosystem. This role leads innovation efforts around cutting-edge AI, owning the architecture and strategy for fine-tuning, retrieval-augmented generation (RAG), agentic frameworks, and domain-specific model adaptation. The specialist will guide the development of high-impact prototypes, oversee the evolution of scalable LLM pipelines, and ensure robust governance, security, and performance across all model implementations. Partnering with engineering, product, and data teams, this position provides technical leadership, evaluates emerging LLM technologies, sets best practices, and helps drive transformation through the practical, safe, and effective deployment of generative AI.

Requirements:
  • All candidates must pass public trust clearance through the U.S. Federal Government. This requires candidates to either be U.S. citizens or pass clearance through the Foreign National Government System which will require that candidates have lived within the United States for at least 3 out of the previous 5 years, have a valid and non-expired passport from their country of birth and appropriate VISA/work permit documentation.
  • Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related technical field, with 5+ years of relevant experience in software engineering, systems engineering, AI/ML, or a related discipline.
  • Experience designing, developing, integrating, and supporting Large Language Model (LLM) and Generative AI solutions, including transformer-based architectures.
  • Experience with LLM fine-tuning, prompt engineering, and model evaluation, including techniques such as LoRA, PEFT, or similar approaches.
  • Experience designing and implementing Retrieval-Augmented Generation (RAG) solutions, including embeddings, vector databases, retrieval pipelines, and search optimization.
  • Strong proficiency in Python and experience with LLM frameworks such as LangChain, LlamaIndex, or comparable technologies.
  • Experience developing and deploying AI/ML solutions in cloud environments such as AWS, Azure, or GCP, including scalable model training, inference, and performance optimization.
  • Knowledge of MLOps/LLMOps practices, including source control, CI/CD, automated testing, monitoring, model lifecycle management, security, and responsible AI practices.

Desired Qualifications:

  • Experience implementing multi-agent or agentic AI systems for task automation and reasoning.
  • Familiarity with LLM evaluation frameworks, structured benchmarking, or human-in-the-loop refinement methods (e.g., RLHF-style workflows).
  • Expertise with advanced retrieval techniques such as hybrid search, graph retrieval, or long-context optimization.
  • Experience optimizing model inference through quantization, model compression, or model distillation.
  • Background integrating LLM services with large-scale analytics environments (e.g., Databricks, Snowflake, Spark).
  • Strong skills in exploratory data analysis, feature engineering, and data modeling to support domain-specific LLM customization.
  • Experience developing innovative prototypes or POCs that leverage state-of-the-art generative AI approaches.
  • Exposure to emerging architectures such as mixture-of-experts models, long-context transformers, or experimental generative frameworks.


Working Environment:
eSimplicity supports a remote work environment operating within the Eastern time zone so we can work with and respond to our government clients. Expected hours are 9:00 AM to 5:00 PM Eastern unless otherwise directed by manager.


Occasional travel for training and project meetings. It is estimated to be less than 5% per year.


Benefits:
eSimplicity offers a comprehensive benefits package, including medical, dental, and vision coverage, 401(k) retirement benefits, paid time off, paid holidays, life and disability insurance, and additional wellness and employee support programs. Eligibility may vary based on employment status and applicable plan terms.


Reasonable Accommodation:
eSimplicity is committed to providing reasonable accommodations to qualified individuals with disabilities during the application and hiring process. Applicants who need assistance or an accommodation should contact Human Resources.


Equal Employment Opportunity:
eSimplicity is an Equal Opportunity Employer, including disability and protected veteran status. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran status, disability, or any other legally protected status.