Platform Engineer - Enterprise Data & AI

About Qlevia AI Qlevia AI is building the semantic brain for enterprise AI — an enterprise context and knowledge graph platform that connects fragmented data, entities and relationships into a unified, trusted and governed operational model. By combining semantic technologies, knowledge graphs and AI, Qlevia enables enterprises to provide applications, users and AI agents with reliable, explainable and context-rich intelligence for decision-making and automation. We are building Qlevia into an enterprise-ready SaaS platform that makes these capabilities accessible through familiar platform services, APIs and interfaces, enabling organisations to integrate, govern and use their data without needing to manage the complexity of the underlying semantic architecture. Please send CV to ***email_hidden*** The Role We are looking for a Platform Engineer (Enterprise Data & AI) to join the core product engineering team and help take the Qlevia platform from its current foundation to an enterprise-ready SaaS product. The role spans the full engineering lifecycle: research, design, prototype, build, integrate, deploy and operate platform capabilities. You will work across the product technology roadmap, exploring and validating new technical approaches where required, while turning proven engineering components and concepts into repeatable, production-grade platform services. Key responsibilities • Contribute to the architecture, design and development of Qlevia platform capabilities. • Conduct applied technical research and experimentation to evaluate emerging technologies, architectural approaches and implementation patterns relevant to the platform, progressing promising concepts into prototypes and production capabilities. • Build and productise reusable platform components, APIs, services and data workflows. • Work across areas such as data acquisition and integration, knowledge graphs, developer interfaces, AI/agent integration, access control and platform services. • Develop and operate cloud-native services and deployment environments. • Improve reliability, observability, testing, automation and operational readiness. • Collaborate with other engineers to integrate existing Qlevia and Zazuko technology into a coherent SaaS platform. • Support technical validation, customer use cases and early enterprise deployments where required. • Contribute to engineering standards, documentation and technical design decisions as the platform and team scale. What we are lookingfor We are looking for a strong software/platform engineer rather than a narrow specialist. You should be comfortable working across several layers of a modern platform, investigating unfamiliar technical areas, and turning ideas or prototypes into reliable product capabilities. Relevant experience may include software engineering, APIs and backend services, cloud and Kubernetes, data engineering, knowledge graphs and semantic technologies, AI/LLM systems, DevOps, full-stack development, or applied technical research. Experience with all of these is not required. More important is strong engineering judgement, curiosity, an ability to work across system boundaries, and a bias toward building, validating and operating production-quality software.