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About the Role
This role focuses on fine-tuning and improving self-hosted large language models that power personalized 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.
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 behaviours
- Diagnose model failures and optimise 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
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 optimisation 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 behaviour issues and translate findings into measurable improvements
Skills
- Python
- PyTorch
- Hugging Face
- Supervised Fine-Tuning (SFT)
- LoRA
- QLoRA
- DPO
- GRPO
- GPU-based training
- Model debugging
- Model evaluation
- Training datasets
- Prompt engineering
- Classification
Location
- Europe
- Remote
Work Type
- Remote
- B2B engagement
Experience Level
- Mid-level
- Senior
Benefits
- Fully remote working within Europe
- Long-term collaboration, with B2B contracting preferred
- 20 working days of paid leave
- $100 monthly health insurance allowance
- Equipment budget
- Access to AI development tools
- Learning and development support
- Annual team gathering
- Opportunity to work on self-hosted LLMs powering consumer-facing AI products
About the Company
- David Kennedy Recruitment is working with a company developing AI-powered conversational products.