EU remote
Senior LLM AI Engineer
About this role
Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem. 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 model development and performance. Optimize LLM inference pipelines for production constraints, including latency, cost, throughput, and infrastructure efficiency. Deploy, operate, monitor, and improve LLM systems in production environments. Translate business challenges into LLM system designs and technical trade-offs. Guide architectural decisions and contribute to the technical direction of applied AI solutions.