Senior AI LLM Engineer — Remote, Self-Hosted Models

David Kennedy Recruitment; Paris; Remote; EUR 90,000 - 150,000; Full time

Benefits offered by this job

Remote within Europe

Long-term collaboration (B2B)

Paid leave 20 days

Health insurance allowance $100/mo

Equipment budget

Learning and development support

Annual team gathering

Work on self-hosted LLMs powering AI

Job summary

David Kennedy Recruitment is seeking a Mid/Senior AI LLM Engineer to join a Europe-wide, remote team. The role focuses on fine-tuning and improving self-hosted large language models powering personalised AI conversations.

You will work hands-on with open-weight models, training datasets, evaluation frameworks and GPU-based training, taking model improvements from experimentation through to production deployment.

Qualifications

• Proven hands-on experience fine-tuning open-weight AI language models and deploying to production.
• Strong Python and ML framework experience.
• Experience with supervised fine-tuning, LoRA/QLoRA, and preference-based training (DPO/GRPO).

Responsibilities

• Fine-tune and improve open-weight language models for production use.
• Prepare and refine training datasets for natural, accurate responses.
• Develop testing methods to measure model quality and identify improvements.
• Improve memory, personality consistency, and long-running dialogue.
• Support multilingual capabilities and multi-character interactions.
• Experiment with training prompts and data; analyze failures and optimize.
• Run GPU-based training experiments and troubleshoot issues.
• Deploy models to production and monitor performance.

Skills

Python programming

PyTorch

Hugging Face

LoRA/QLoRA techniques

DPO/GRPO

Dataset preparation

GPU-based training

Model evaluation

Tools

CUDA

PyTorch Lightning

Git

Job description

David Kennedy Recruitment is seeking a Mid/Senior AI LLM Engineer to join a Europe-wide, remote team. The role focuses on fine-tuning and improving self-hosted large language models powering personalised AI conversations.

You will work hands-on with open-weight models, training datasets, evaluation frameworks and GPU-based training, taking model improvements from experimentation through to production deployment.