Mid/Senior AI LLM Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Mid/Senior AI LLM Engineer based in Italy.

As a Mid/Senior AI LLM Engineer, you will develop, fine-tune, and optimize self-hosted large language models that power personalized, engaging AI conversations. You will work across the full model development lifecycle, from preparing training datasets and running GPU-based experiments to evaluating performance and deploying improvements into production. Your work will help enhance conversational quality, long-term memory, personality consistency, and multilingual interactions. Collaborating with AI engineers, product specialists, web developers, and DevOps teams, you will turn experimental findings into reliable, scalable solutions. This hands-on role offers the opportunity to work with open-weight models, advanced fine-tuning techniques, and practical AI evaluation frameworks. You will join a remote-first environment focused on building innovative, consumer-facing AI products while continuously improving model performance and user experience.

Accountabilities

• Fine-tune and optimize existing open-weight large language models to improve conversational quality, response accuracy, consistency, and personalization.

• Prepare, curate, and enhance training datasets to support effective model learning, reduce undesirable outputs, and improve the relevance and naturalness of generated responses.

• Design and implement evaluation frameworks, benchmarks, and testing methodologies to measure model quality, identify weaknesses, and validate improvements.

• Improve conversational memory, personality consistency, and the ability of models to maintain coherent, context-aware interactions over extended conversations.

• Develop multilingual conversational capabilities and support interactions involving multiple characters, ensuring natural and consistent behavior across different scenarios.

• Investigate model failures and performance limitations, adjusting training data, prompts, model configurations, and response filters to address identified issues.

• Conduct GPU-based training and fine-tuning experiments, troubleshoot infrastructure and performance issues, and optimize training workflows for efficiency and reliability.

• Apply modern model adaptation and alignment techniques, including supervised fine-tuning, LoRA, QLoRA, and preference-based optimization methods where appropriate.

• Deploy improved models into production environments, monitor their performance, analyze user-facing outcomes, and implement iterative refinements based on evaluation results.

• Collaborate with AI engineering, product, web development, and DevOps teams to integrate model improvements into consumer-facing applications and align technical work with product objectives.

• Work with sensitive conversational use cases and moderation requirements, balancing model behavior, user experience, and applicable content policies.

Requirements:

• Proven hands-on experience fine-tuning open-weight large language models and deploying models into production environments.

• Strong Python programming skills, including experience developing, testing, and maintaining machine-learning workflows.

• Practical experience with PyTorch and Hugging Face or comparable machine-learning frameworks and model development tools.

• Experience with model training and adaptation techniques such as supervised fine-tuning (SFT), Low-Rank Adaptation (LoRA), and Quantized Low-Rank Adaptation (QLoRA).

• Familiarity with preference-based training and alignment techniques, such as Direct Preference Optimization (DPO) or Group Relative Policy Optimization (GRPO).

• Experience preparing and managing training datasets, improving data quality, and developing evaluation frameworks to assess model behavior and output quality.

• Practical experience with GPU-based model training, performance optimization, and debugging training-related technical issues.

• Strong analytical and problem-solving skills, with the ability to investigate model behavior, identify root causes, design experiments, and demonstrate measurable improvements.

• Understanding of the challenges involved in deploying and maintaining LLM-powered applications, including response quality, consistency, inference performance, and ongoing monitoring.

• Experience working on consumer-facing AI products, with an understanding of how model quality and conversational behavior affect user experience.

• Comfort working with minimally restricted or uncensored AI models, including explicit adult or NSFW content and sensitive moderation materi