Everpure

SeniorLLMAIEngineer

$1300–1950k ~AI est. Prague, Czech Republic
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior LLM AI Engineer at Everpure”

Full Job Description

We’re in an unbelievably exciting area of tech and are fundamentally reshaping the data storage industry. Here, you lead with innovative thinking, grow along with us, and join the smartest team in the industry. This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us. THE ROLE 1touch.io is a technology company focused on automated, real-time discovery, mapping, and tracking of sensitive personal data. Its AI-powered platform helps enterprises improve data privacy, security, and governance across complex on-premises and cloud environments. Together, 1touch.io and Everpure transform enterprise data from passive storage into an intelligent, context-aware, and governed foundation - making it AI-ready at the source so organizations can securely understand, trust, and activate their data at scale. The Senior LLM / AI Engineer designs, builds, and deploys production-ready AI systems and workflows that transform business requirements into scalable and reliable LLM-driven solutions. This role is responsible for the full lifecycle of AI and LLM-based solutions- from solution architecture and experimentation through model fine-tuning, optimization, and production deployment. The focus is on developing business intelligence assistants and process automation workflows using private LLM APIs and proprietary fine-tuned models. WHAT YOU'LL DO Design, build, and deploy LLM-based AI solutions for natural language interaction, search, analytics, and reasoning across structured, unstructured, and graph-based enterprise data sources. Implement workflows supporting model lifecycle activities, including data annotation, synthetic dataset generation, and model evaluation. Lead the fine-tuning and continuous improvement of proprietary LLMs to meet privacy, quality, and latency requirements. Define model training strategies and dataset requirements to support effective mode

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