Remote AI LLM Engineer: Fine-Tune & Deploy Self-Hosted Models

David Kennedy Recruitment; United States; Remote; USD 101,000 - 169,000; Full time

Benefits offered by this job

Health insurance allowance

Equipment budget

Access to AI development tools

Learning and development support

Annual team gathering

Job summary

David Kennedy Recruitment seeks a Mid/Senior AI LLM Engineer to work remotely within Europe on fine-tuning self-hosted LLMs powering consumer AI conversations. You will train and evaluate models using GPUs, develop data pipelines, and push improvements from experimentation to production deployment.

The role requires hands-on ML experience with open-weight models, PyTorch, and Hugging Face, plus strong Python skills and a track record of deploying models in production environments.

Qualifications

• Hands-on experience fine-tuning open-weight AI language models and deploying them to production.
• Strong Python programming skills and practical ML tooling experience.
• Experience with PyTorch and Hugging Face or similar frameworks.

Responsibilities

• Train and fine-tune language models to improve conversational abilities.
• Prepare and improve training datasets for accurate, natural responses.
• Develop testing methods to measure model quality and identify improvements.
• Improve memory, personality consistency and long-running dialogues.
• Develop multilingual capabilities and interactions among multiple characters.
• Investigate failures and optimize data, prompts and filters.
• Run GPU-based training experiments and troubleshoot issues.
• Deploy improved models into production and monitor performance.

Skills

Python programming

Open-weight LLMs

GPU-based training

Model evaluation

Multilingual capabilities

Tools

PyTorch

Hugging Face

Job description

David Kennedy Recruitment seeks a Mid/Senior AI LLM Engineer to work remotely within Europe on fine-tuning self-hosted LLMs powering consumer AI conversations. You will train and evaluate models using GPUs, develop data pipelines, and push improvements from experimentation to production deployment.

The role requires hands-on ML experience with open-weight models, PyTorch, and Hugging Face, plus strong Python skills and a track record of deploying models in production environments.