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Llm Fine Tune Model Jobs (NOW HIRING)

Lead AI Engineer

Bellevue, WA · On-site

$115K - $151K/yr

... Fine-tune models using techniques such as LoRA, PEFT, and instruction tuning. • Develop and evaluate embedding models for similarity search and semantic retrieval. • Conduct LLM evaluation using ...

Prompt Engineer Jobs

Manhattan, NY · On-site

$120 - $180/hr

... fine-tune models for specific use cases, and ensure AI outputs are accurate, safe, and cost ... Strong writing and analytical skills, Python programming, understanding of LLM architectures ...

Applied AI Engineer

Palo Alto, CA · On-site

$180K - $250K/yr

Build & fine-tune models. Fine-tune foundation models on proprietary data and implement novel ... Demonstrated hands-on experience building and deploying LLM-based systems (fine-tuning, RAG, or ...

AI/ML Engineer

Plano, TX · On-site

$109K - $131K/yr

... RAG), LLM fine-tuning, and intelligent enterprise applications. Key Responsibilities * Design ... Fine-tune foundation models and implement Retrieval-Augmented Generation (RAG) architectures.

Senior AI Engineer Agentic Systems

Plano, TX · On-site

$100K - $137K/yr

... fine-tune models, debug model behavior, and evaluate technical tradeoffs. • Experience with LLM serving and inference using vLLM or comparable technologies such as TGI or TensorRT-LLM. • ...

New

Senior Machine Learning Engineer

Boston, MA · Remote

$125K - $165K/yr

Format data appropriately for the chosen LLM and training pipeline * Model Training & Fine-Tuning: Design, train, and fine-tune various LLMs on extensive healthcare data to solve specific clinical or ...

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Format data appropriately for the chosen LLM and training pipeline * Model Training & Fine-Tuning: Design, train, and fine-tune various LLMs on extensive healthcare data to solve specific clinical or ...

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Format data appropriately for the chosen LLM and training pipeline * Model Training & Fine-Tuning: Design, train, and fine-tune various LLMs on extensive healthcare data to solve specific clinical or ...

Fine-tune and evaluate LLMs using both open-source and proprietary data * Collaborate with product ... Monitor and improve model quality, latency, explainability, and safety * Stay ahead of the curve in ...

Showing results 41-60

Llm Fine Tune Model information

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$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.

Lead AI Engineer

ZS

Bellevue, WA • On-site

$115K - $151K/yr

Full-time

Re-posted 24 days ago


Job description

Job Summary:
ZS is a management consulting and technology firm focused on improving life through data, science, and technology. The Lead AI Engineer will lead the development of production-grade LLM systems, focusing on building and optimizing AI applications and pipelines.
Responsibilities:
• Design and implement LLM-powered applications using state-of-the-art transformer models.
• Build and optimize RAG pipelines using embeddings, chunking strategies, and vector search.
• Experiment with prompt engineering, structured outputs (JSON schemas/function calling), and tool-augmented LLMs (agents/workflows).
• Fine-tune models using techniques such as LoRA, PEFT, and instruction tuning.
• Develop and evaluate embedding models for similarity search and semantic retrieval.
• Conduct LLM evaluation using automated and human-in-the-loop techniques (offline + online).
• Optimize inference workflows for latency, GPU utilization, and cost efficiency (quantization, batching, caching).
• Build and maintain REST API Services (FastAPI etc.) to deploy LLM/RAG endpoints, integrate with product systems, and support scalable inference.
• Contribute to integration of AI systems into production software environments (CI/CD, monitoring, reliability).
• Research and prototype cutting-edge approaches in Generative AI and share learnings with the team.
Qualifications:
Required:
• A master's or bachelor's degree in Computer Science or related field from a top university
• 4+ years' hands-on experience in Machine Learning (ML) with production LLM systems
• Good fundamentals of machine learning, deep learning and fine tuning models (LLM) including: Understanding of transformer architectures, Prompt engineering expertise, Embeddings and vector search
• Experienced in backend API design with FastAPI, async patterns, rate limiting
• Experience with vector DB including: Pinecone, Weaviate, or Chroma, Embedding storage and similarity search, Hybrid search implementations
• Strong programming expertise in Python is must including: Async programming (asyncio, async/await), Type hints and Pydantic, SOLID principles and design patterns
• Experience in ML Ops to measure and track model performance including: MLFlow for model tracking, Langfuse for LLM observability (strongly preferred), Model versioning and A/B testing
• Experience in working with NLP & computer vision
• Fluency in English
• Client-first mentality
• Intense work ethic
• Collaborative spirit and problem-solving approach
Preferred:
• Langfuse for LLM observability
Company:
ZS is a management consulting and technology firm that partners with companies to improve life and how we live it. Founded in 1983, the company is headquartered in Evanston, USA, with a team of 10001+ employees. The company is currently Late Stage.

ZS logo

About ZS

Sourced by ZipRecruiter

Industry

Business management consulting

Company size

10,000+ Employees

Headquarters location

Evanston, IL, US

Year founded

1983