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Llm Developer Jobs in Decatur, GA (NOW HIRING)

Outlier AI

OpenClaw LLM Engineer (Remote)

Atlanta, GA · Remote

Whether you are a passionate orchestration guru or experienced software developer -- we want you to help us train the world's most advanced generative systems. Ideal Qualifications * 2+ years of ...

Prolego

GenAI Systems Developer

Atlanta, GA · Remote

$185K - $215K/yr

Prolego is seeking Gen AI Systems Developers to design, build, and customize cutting-edge AI ... Develop custom methods to test and validate LLM performance. * Interfaces & Integrations: Create ...

Insight Global

Full Stack Developer

Atlanta, GA

Build backend services in Python or Node that orchestrate agent interactions; integrate with LLM ... Strong backend engineering skills in Python and Node. * Experience integrating with APIs, async ...

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How much do llm developer jobs pay per hour?

As of May 30, 2026, the average hourly pay for llm developer in Decatur, GA is $48.98, according to ZipRecruiter salary data. Most workers in this role earn between $38.51 and $59.38 per hour, depending on experience, location, and employer.

What does an LLM Developer do?

An LLM Developer designs, fine-tunes, and implements large language models (LLMs) for various applications, such as chatbots, content generation, and AI-driven tools. They work with machine learning frameworks, optimize model performance, and ensure efficient deployment. This role requires expertise in natural language processing (NLP), deep learning, and programming languages like Python.

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

To excel as an LLM Developer, you need strong expertise in natural language processing (NLP), deep learning frameworks, and programming languages such as Python, typically supported by a degree in computer science or a related field. Familiarity with machine learning libraries (like TensorFlow or PyTorch), cloud computing platforms, and experience with prompt engineering or fine-tuning large language models is crucial. Excellent problem-solving abilities, collaboration, and effective communication skills help you design solutions and work efficiently within multidisciplinary teams. These qualifications are essential for successfully building, deploying, and optimizing large language models that drive impactful AI applications.

What are the typical daily tasks and responsibilities of an LLM Developer?

As an LLM Developer, your daily responsibilities often include designing, fine-tuning, and evaluating large language models to meet specific application needs. You may work on tasks such as data preprocessing, model training, performance benchmarking, and error analysis, frequently collaborating with data scientists, research engineers, and product managers. Keeping up to date with the latest advancements in NLP and integrating new techniques into production models is also a key part of the role. These tasks are usually performed in a team-oriented environment where clear communication and iterative experimentation are highly valued.

What is the salary of LLM engineer?

The salary of an LLM (Large Language Model) engineer typically ranges from $100,000 to $180,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in deep learning and NLP can earn higher compensation, often exceeding $200,000 with bonuses and stock options.
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Llm Developer jobs near you

LLM Engineer (GCP Preferred)

Krest Global Solutions

Atlanta, GA • Hybrid

$58 - $60/hr

Contractor

Posted yesterday

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.