AI​/LLM Engineer; Model Fine-Tuning & Conversational AI

We are looking for an experienced AI/LLM Engineer (Model Fine-Tuning & Conversational AI) to join a fast-growing international technology company developing AI-powered conversational products. This is a hands-on engineering role focused on training, fine-tuning and improving self-hosted, open-weight large language models (LLMs) that power personalised AI conversations. You will work directly with model training pipelines, datasets, evaluation frameworks and GPU infrastructure to improve conversational quality, personality consistency, contextual memory and multilingual capabilities.

The position covers the full model improvement lifecycle, from identifying weaknesses and preparing training data to running experiments, measuring results and deploying improved models into production. You will collaborate closely with AI engineering, product, web development and Dev Ops teams to deliver measurable improvements in conversational performance and user experience. The ideal candidate has practical experience personally fine-tuning and deploying open-weight LLMs, strong Python and PyTorch skills, and a solid understanding of post-training techniques such as supervised fine-tuning, LoRA and QLoRA.

This is a model engineering position rather than a prompt engineering, API integration or purely academic research role. Candidates must have direct experience working with model weights, training processes and production deployment.

• 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 to enhance response accuracy, naturalness and consistency.

• Design and implement evaluation frameworks to measure model quality and identify opportunities for improvement.

• 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 techniques such as 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 Dev Ops specialists on model integration and deployment.

• Research and evaluate relevant 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…