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

Job not on LinkedIn

🔥 0 minutes ago

🇳🇱 Netherlands – Remote

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

🗣️ LLM Engineer

👻 Ghost score 25%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Salve.Inno

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2024

💼 Consulting

📣 Marketing

📦 Logistics

Consulting • Marketing • Logistics

Salve. Inno is a recruitment and consulting firm that connects exceptional talent with businesses through personalized hiring strategies and global remote sourcing. The company specializes in recruitment for roles across sectors such as marketing, forex, and iGaming, offering candidate sourcing, screening, and career-site driven hiring experiences while emphasizing DE&I, communication, and innovative process building. Founded in 2024 and headquartered in Gdańsk, Poland, Salve. Inno operates with a small team and a global footprint via remote job listings and consulting services.

📋 Description

• 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

🎯 Requirements

• 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

🏖️ Benefits

• 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