Mid/Senior AI LLM Engineer

Jobgether; Italy; Remote; EUR 100,000 - 140,000; Full time

Benefits offered by this job

Fully remote across Europe

Long-term collaboration (B2B possible)

25 days paid leave

Monthly health insurance allowance $-

Equipment budget

Access to AI development tools

Learning and development support

Annual team gathering

Hands-on with self-hosted LLMs

Challenging ML environment

Job summary

Jobgether is seeking a Mid/Senior AI LLM Engineer based in Italy to develop, fine-tune, and optimize self-hosted LLMs powering consumer-facing AI products. You will work across the full model lifecycle, from data prep to GPU-based experiments and production deployment, improving memory, multilingual support, and personality consistency.

You will collaborate with AI, product, web, and DevOps teams, evaluate model quality with robust benchmarks, and iterate improvements in a remote-first

Qualifications

• Hands-on experience fine-tuning open-weight LLMs and deploying to production.
• Proficient in Python and ML workflows.
• Experience with PyTorch and Hugging Face or equivalent frameworks.
• Knowledge of SFT, LoRA, QLoRA and alignment methods like DPO/GRPO.
• Experience preparing data, creating evaluation frameworks, and monitoring models in production.
• GPU-based training, performance optimization, and debugging.
• Strong analytical, problem-solving and communication skills.
• Experience with consumer-facing AI products and moderation considerations.
• Ability to work autonomously in a remote environment.

Responsibilities

• Fine-tune and optimize open-weight LLMs to improve conversational quality, accuracy, consistency, and personalization.
• Prepare and enhance training datasets to improve learning and reduce undesired outputs.
• Design evaluation frameworks, benchmarks, and testing methodologies.
• Improve conversational memory and personality consistency across long chats.
• Develop multilingual conversational capabilities and cross-character interactions.
• Investigate failures and adjust data, prompts, and configurations accordingly.
• Run GPU-based training and fine-tuning experiments; troubleshoot infrastructure.
• Apply modern adaptation techniques (SFT, LoRA, QLoRA, alignment methods).
• Deploy models to production, monitor performance, and iterate improvements.
• Collaborate with AI, product, web, and DevOps teams to integrate improvements.
• Handle sensitive or moderated content with safeguards.

Skills

Python

PyTorch

Hugging Face

SFT

LoRA

QLoRA

DPO

GRPO

GPU training

Model evaluation

Remote work

Data preparation

Tools

Hugging Face

PyTorch

Job description

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 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.

Benefits:

• Fully remote working arrangement within Europe.

• Long-term collaboration opportunities, with B2B contracting preferred.

• 20 working days of paid leave.

• A monthly health insurance allowance of $100.

• Equipment budget to support your remote working setup.

• Access to AI development tools and technologies.

• Learning and development support to encourage continuous professional growth.

• Annual team gathering to connect and collaborate with colleagues.

• Opportunity to work hands-on with self-hosted LLMs powering innovative, consumer-facing AI products.

• A technically challenging environment offering exposure to modern language models, fine-tuning methods, and conversational AI development.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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