Mid/Senior AI LLM Engineer

NetherlandsRemoteFull-timesenior

Data & AILLM Engineer

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Requirements Summary

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,

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Data & AILLM Engineer

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.

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

~2 min read

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 material, while applying appropriate safeguards and content-handling practices.
Ability to work independently in a remote environment, manage experiments effectively, document findings, and collaborate with cross-functional teams.
Strong communication skills, attention to detail, and a continuous-learning mindset, with the ability to translate experimental results into practical product improvements.