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Llm Fine Tune Model Jobs (NOW HIRING)

WI · On-site

$80 - $120/hr

Fine-tune models using available datasets to improve accuracy for specific business domains ... Monitor LLM performance through logs, metrics, and analytics dashboards. * Test model outputs for ...

AI Engineer

Menlo Park, CA · On-site

$100 - $150/hr

Fine-tune proprietary and open-source large language models for travel-specific applications ... Experience with LLM fine-tuning, RLHF, and prompt engineering * Knowledge of model optimization ...

Key Responsibilities: * Independently build, train, and fine-tune AI/ML models. * Apply data ... Experience with NLP, LLM, or GenAI tools (e.g. LoRA, LangChain, RAG, LLM Fine Tuning). * Hands-on ...

Fine-tune foundation models on proprietary data and implement novel techniques to achieve world ... and deploying LLM-based systems (fine-tuning, RAG, or agentic workflows) in a production ...

Fine-tune models for performance and efficiency. * Troubleshooting: Address and resolve issues related to generative AI models and implementations. * Documentation: Create and maintain comprehensive ...

Fine-tune models for performance and efficiency. * Troubleshooting: Address and resolve issues related to generative AI models and implementations. * Documentation: Create and maintain comprehensive ...

Senior LLM Engineer

Austin, TX · On-site

$195K - $255K/yr

Fine-tune foundation models (LoRA/QLoRA, RLHF/DPO, instruction tuning) for domain-specific tasks and terminology. * Build agentic and tool-use workflows that connect the LLM to internal engineering ...

New

$150 - $190/hr

... models * Evaluate reasoning approaches, including latent space reasoning * Pre-train, fine-tune, and modify the State-of-the-Art LLMs * Optimizing and scaling LLM pipelines * Adjust frameworks and ...

New

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Llm Fine Tune Model information

See salary details

$140.5K

$166.2K

$193.5K

How much do llm fine tune model jobs pay per year?

As of Aug 23, 2026, the average yearly pay for llm fine tune model in the United States is $166,249.00, according to ZipRecruiter salary data. Most workers in this role earn between $158,000.00 and $170,000.00 per year, depending on experience, location, and employer.

What is an LLM fine-tune model?

An LLM fine-tune model refers to a large language model (LLM) that has been further trained on a specific dataset to specialize in certain tasks or domains. Fine-tuning allows organizations or individuals to adapt a general-purpose LLM, such as GPT or BERT, to better understand and respond to domain-specific language, requirements, or user needs. This process improves the model's accuracy, relevance, and usefulness for specialized applications. Fine-tuning typically involves using transfer learning techniques and requires a curated dataset for the desired task.

What are the key skills and qualifications needed to thrive as an LLM fine-tune model engineer?

To thrive as an LLM Fine-Tune Model Engineer, you need a strong background in machine learning, natural language processing, and programming (typically Python), often supported by a degree in computer science or related fields. Experience with deep learning frameworks (such as PyTorch or TensorFlow), model evaluation tools, and familiarity with cloud platforms or MLOps tools is essential. Analytical thinking, attention to detail, and effective communication help in troubleshooting, interpreting results, and collaborating with cross-functional teams. These skills ensure the development of robust, accurate, and scalable language models tailored to specific business needs.

What are some common challenges faced when fine-tuning large language models (LLMs) in a professional setting?

Fine-tuning large language models often involves handling vast datasets, ensuring data privacy, and balancing computational resource constraints. Professionals in this role must troubleshoot issues related to overfitting, bias in training data, and model drift. Collaboration with data engineers, domain experts, and MLOps teams is crucial to ensure the model meets specific business needs while maintaining ethical and performance standards.

What is the difference between Llm Fine Tune Model vs Data Scientist?

AspectLlm Fine Tune ModelData Scientist
Required CredentialsKnowledge of machine learning, NLP, programming skillsStatistics, programming, domain expertise
Work EnvironmentAI development teams, research labsBusiness, research, analytics teams
Industry UsageAI, tech companies, startupsFinance, healthcare, marketing, tech

The main difference is that an Llm Fine Tune Model focuses on customizing large language models for specific tasks, while a Data Scientist analyzes data to generate insights and build models. Both roles require programming and analytical skills, but their applications and focus areas differ significantly.

Infographic showing various Llm Fine Tune Model job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 13% Part Time, and 2% Contract. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution, with an average salary of $166,249 per year, or $79.9 per hour.

LLM (ChatGPT) Application Engineer

Devitechs

WI • On-site

$80 - $120/hr

Other

Posted 5 days ago


Job description

Responsibilities
  • Design and develop end-to-end LLM-powered applications using GPT-4, GPT-5 or custom models.
  • Integrate ChatGPT with backend systems through REST APIs, WebSockets, and cloud functions.
  • Build scalable AI pipelines for summarization, content generation, Q&A, and automation tasks.
  • Fine-tune models using available datasets to improve accuracy for specific business domains.
  • Implement guardrails, moderation rules, and safety filters to control model output.
  • Build vector search systems using FAISS, Pinecone, Weaviate, or Milvus.
  • Work with RAG (Retrieval Augmented Generation) frameworks for grounded responses.
  • Implement caching, batching, and optimization for cost reduction and performance.
  • Develop structured workflows for approvals, escalations, and error handling in LLM interactions.
  • Monitor LLM performance through logs, metrics, and analytics dashboards.
  • Test model outputs for correctness, consistency, and hallucination risks.
  • Collaborate with product and design teams to create LLM-driven UX flows.
  • Maintain secure handling of sensitive data used in model queries.
  • Implement multi-agent systems for complex AI interactions and decision-making.
  • Automate repetitive business tasks using ChatGPT-powered agents.
  • Document data flows, architecture, model behaviors, and system dependencies.
  • Troubleshoot model-related issues including context loss and misclassification.
  • Scale LLM applications using Kubernetes, serverless functions, and cloud orchestration.
  • Stay updated with the latest GPT model releases, research papers, and API features.
  • Provide internal training on LLM capabilities, limitations, and best practices.
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