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Entry Level Large Language Model Llm Jobs (NOW HIRING)

AMSYS Talent

LLM Infrastructure Engineer

Houston, TX · On-site

$97.10K - $127.40K/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 ...

Straive

LLM Engineer

San Francisco, CA · On-site +1

The ideal candidate will have a deep understanding of large language models (LLMs) and their application in real-world scenarios. As an LLM Engineer at Straive, you will play a crucial role in ...

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

As of May 31, 2026, the average hourly pay for entry level large language model llm in the United States is $22.48, according to ZipRecruiter salary data. Most workers in this role earn between $19.47 and $24.76 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Entry Level Large Language Model (LLM) Engineer, and why are they important?

To thrive as an Entry Level Large Language Model (LLM) Engineer, you need a solid background in computer science, machine learning fundamentals, and proficiency in programming languages like Python, typically supported by a relevant degree. Familiarity with machine learning frameworks (such as PyTorch or TensorFlow), version control systems, and cloud computing platforms is often required. Strong analytical thinking, problem-solving skills, and effective communication set candidates apart in this role. These competencies are crucial for developing, fine-tuning, and deploying LLMs to ensure innovative and reliable AI solutions.

What types of projects do entry-level professionals working with Large Language Models (LLMs) typically contribute to?

Entry-level professionals in LLM roles often support data preparation, model fine-tuning, and evaluation tasks under the guidance of more experienced engineers or data scientists. They may annotate data, help run experiments, monitor model outputs for quality, and assist in deploying models for internal testing or limited production use. Collaboration with cross-functional teams—including machine learning engineers, product managers, and research scientists—is common, offering valuable exposure to various stages of the LLM development lifecycle. This hands-on experience helps build foundational skills and prepares individuals for more advanced responsibilities in the field.

What is an Entry Level Large Language Model (LLM) role?

An Entry Level Large Language Model (LLM) role typically refers to positions where individuals work with advanced AI systems, like ChatGPT or similar models, to support tasks such as data annotation, model evaluation, prompt engineering, or customer support. Entry-level LLM professionals might help train models, test outputs for accuracy, or assist with basic research. These roles usually require strong analytical skills, attention to detail, and some familiarity with AI concepts, but do not always require advanced programming experience. They offer a great starting point for those interested in the field of artificial intelligence and natural language processing.

What is the difference between Entry Level Large Language Model Llm vs Data Analyst?

AspectEntry Level Large Language Model LlmData Analyst
Required CredentialsBasic understanding of NLP, programming skills (Python), coursework or certifications in AI/MLBachelor's degree in Data Science, Statistics, or related field; often certifications in data analysis tools
Work EnvironmentResearch labs, AI companies, tech startups; focus on model development and trainingBusiness environments, consulting firms, finance, healthcare; focus on data interpretation and reporting
Industry UsageAI development, NLP applications, machine learning researchBusiness intelligence, market analysis, operational insights

Entry Level Large Language Model Llm roles focus on developing and training NLP models, requiring programming and AI knowledge. Data Analysts interpret data to inform business decisions, often using statistical tools. While both roles involve working with data, Llm positions are more technical and research-oriented, whereas Data Analysts focus on data interpretation and reporting.

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Entry Level Large Language Model Llm jobs near you
Infographic showing various Entry Level Large Language Model Llm job openings in the United States as of May 2026, with employment types broken down into 72% Full Time, 23% Part Time, and 5% Contract. Highlights an 76% Physical, 1% Hybrid, and 23% Remote job distribution, with an average salary of $46,753 per year, or $22.5 per hour.

Senior ML Operations Engineer - Python

Texas State Library and Archives Commision

Oakland, CA • On-site

Full-time

Posted 27 days ago

Job description

Job Description:
We are seeking three knowledgeable Senior Python/ML Operations Engineers with advanced Python and Flask, Large Language Models, Open API engineering, Containerization and Swagger expertise for a multi-year engagement to work with a foremost Healthcare IT Solutions Group in their Innovations Project Team.
Prerequisites for the selection of the Consultants encompass:
  • Advanced Python programming for back-end development for Machine Learning Operations.
  • Expertise with OpenAPI specs.
  • Conversant with LLMs, specifically Llama 2 or Mistral.
  • Familiarity with Prompt Engineering.
  • Required to productionize Large Language Model (LLM) based solutions.
  • Capable of taking an open-source Large Language Model (LLM) and fine tuning it to reflect custom data and retrieve data from a Vector database.
  • Will be required to design a RESTful API that interfaces with a Large Language Model (LLM) and can be used for user consumption.
  • Write Helm charts and the web front end.
  • Must be skilled in modularizing machine learning code.
  • Will transform Data Scientists' models into scalable, maintainable systems.
  • Integrate the APIs with MongoDB (Reddis, Vector, databases, embedding models).
  • Should have expertise in deploying and managing containerized applications using Kubernetes.
  • Ability to take an open-source hugging face model and productionalize it so that user can use it on custom data.
  • Plan the builds with Swagger CodeGen, Editor and Inspector.
  • Experience with Jupyter notebooks, Flask, FastAPI, and on premises Docker and Kubernetes.
  • Angular and Node are also utilized in this environment.
  • Document plan and steps for development.

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