AI/LLM Engineer – Model Fine-Tuning, Conversational AI

• Train, fine-tune and optimise existing open-weight language models to improve conversational performance
• Develop and maintain model post-training workflows using supervised fine-tuning and parameter-efficient training techniques
• Prepare, clean, structure and improve training datasets
• Design and implement evaluation frameworks to measure model quality and identify improvement opportunities
• Improve conversational memory, contextual understanding and personality consistency across extended interactions
• Develop and optimise multilingual conversational capabilities
• Enhance interactions involving multiple AI characters and complex dialogue scenarios
• Investigate model failures, inconsistent responses and behavioural issues
• Optimise training data, prompting approaches and response filtering mechanisms based on evaluation findings
• Conduct GPU-based model training experiments and troubleshoot performance, memory and training stability issues
• Apply SFT, LoRA and QLoRA, with exposure to preference optimisation approaches such as DPO or GRPO
• Analyse experimental results and identify measurable improvements in model behaviour and conversational quality
• Deploy improved models into production environments and monitor their performance
• Continuously refine deployed models using evaluation data, user interaction patterns and performance insights
• Collaborate with AI engineers, product teams, web developers and DevOps specialists on model integration and deployment
• Research and evaluate developments in open-weight language models and post-training methodologies

• Proven hands-on experience personally fine-tuning open-weight LLMs and deploying improved models into production
• Strong professional programming skills in Python
• Practical experience using PyTorch and Hugging Face or comparable machine learning frameworks
• Hands-on experience with supervised fine-tuning (SFT) and parameter-efficient fine-tuning techniques such as LoRA and QLoRA
• Familiarity with preference optimisation techniques, particularly DPO or GRPO
• Experience independently preparing training datasets and building model evaluation frameworks
• Practical experience conducting GPU-based training experiments, including debugging training issues
• Strong understanding of model behaviour, conversational quality and post-training optimisation
• Ability to investigate model weaknesses, implement improvements and demonstrate measurable results
• Experience taking model improvements from experimentation through to production deployment
• Previous experience working with consumer-facing AI applications or products
• Ability to work with complex conversational datasets and assess model outputs systematically
• Strong analytical thinking, technical problem-solving skills and attention to detail
• Ability to collaborate effectively with engineering, product and infrastructure teams
• Comfortable taking ownership of model training, evaluation and deployment tasks
• Ability to work independently in a fast-paced, international remote environment
• Based in Europe and willing to work under a B2B contractor agreement
• Preferred: experience fine-tuning models for conversational AI, AI companionship, roleplay or interactive entertainment
• Preferred: experience improving long-context dialogue, memory retention or personality consistency
• Preferred: practical experience with multilingual LLM training and evaluation
• Preferred: experience implementing preference-based optimisation using DPO or GRPO
• Preferred: familiarity with production model monitoring and iterative improvement workflows
• Preferred: experience optimising GPU training efficiency and troubleshooting model training infrastructure
• Preferred: experience with consumer generative AI products serving large user bases

• Fully remote working arrangement within Europe
• Long-term collaboration under a B2B contractor agreement
• 20 working days of paid leave
• $100 monthly health insurance allowance
• Equipment budget to support your remote working setup
• Access to AI development tools
• Learning and professional development support
• Annual team gathering
• Opportunity to work directly with self-hosted LLMs powering consumer-facing conversational AI products
• Hands-on ownership of model training, evaluation and production improvements
• International working environment within a rapidly growing AI technology company
• Opportunity to contribute to innovative developments in conversational AI and LLM post-training