Mid/Senior AI LLM Engineer

David Kennedy Recruitment•Capon Bridge, WV

1d•Remote

About The Position

David Kennedy Recruitment is working with a company developing AI-powered conversational products to onboard a Mid/Senior AI LLM Engineer remotely within Europe. The role focuses on fine-tuning and improving self-hosted, open-weight large language models that power personalized AI conversations. The engineer will work hands-on with models, training datasets, evaluation frameworks, and GPU-based training, taking improvements from experimentation through to production deployment. The position involves collaboration with the LLM Tech Lead, CTO, Product, Web, and DevOps teams.

Requirements

Proven hands-on experience fine-tuning open-weight LLMs and deploying them into production.
Strong Python programming skills.
Practical experience with PyTorch and Hugging Face.
Hands-on experience with GPU-based model training and debugging.
Experience with supervised fine-tuning and parameter-efficient fine-tuning techniques, including LoRA or QLoRA.
Familiarity with preference optimization techniques such as DPO or GRPO.
Ability to independently build training datasets and model evaluation frameworks.
Experience working with consumer-facing AI products.
Comfort working with uncensored AI models, explicit adult/NSFW content, and sensitive moderation material.
Ability to diagnose model behavior issues and translate findings into measurable improvements.

Responsibilities

Fine-tune open-weight large language models using techniques such as supervised fine-tuning (SFT), LoRA, QLoRA, DPO, and GRPO.
Build and maintain training datasets and model evaluation frameworks.
Improve conversational memory, personality consistency, and long-running dialogue quality.
Develop multilingual conversational capabilities and group interaction behaviors.
Diagnose model failures and optimize training data, prompts, and classifiers.
Run GPU-based training experiments, debug issues, and assess model performance.
Deploy improved models into production, measure results, and iterate on performance.
Collaborate with the LLM Tech Lead, CTO, Product, Web, and DevOps teams to deliver model improvements.