Data and Semantic Architect

Company description: Ericsson AB Job description: Join our Team   About this opportunityWe are now looking for a Data and Semantic Architect to join RTE TSM Data and AI Accelerator and shape an AI-ready data foundation across Radio R&D. This key role turns complex engineering and field data into reusable data products, semantic knowledge, and trusted AI capabilities. You will combine architectural leadership with hands-on implementation across cloud and on-prem environments to accelerate high-impact AI use cases and AI-native radio capabilities. What you will do•    Drive the applied architecture blueprint for an AI-ready Radio R&D data foundation, including reusable datasets, semantic models, knowledge graphs, MCP interfaces, and AI/ML consumption patterns.•    Define guidance for connecting product definitions, telecom terminology, configuration, performance, engineering, and field data into consistent, reusable, and governed information models.•    Develop semantic, agent, and MCP patterns, including ontologies, mappings, validation rules, deterministic tools, permission-aware retrieval, and safe execution across cloud and on-prem environments.•    Translate needs across RTE, BNEW, BCSS, and GFTL into roadmap input and actionable guidance for shared datasets, semantic capabilities, and AI-enabled solutions.•    Guide teams in applying the common foundation, using domain-specific evaluation, traceability, observability, and human review to ensure practical, scalable, secure, and compliant solutions.   The skills you bring•    Extensive experience in data architecture, data engineering, distributed data platforms, or applied machine learning, preferably within R&D and telecom, RAN, or Radio.•    Proven ability to implement AI-ready data architectures covering ingestion, storage, distributed processing, reusable data products, ML-ready datasets, semantic layers, knowledge graphs, data quality, access control, governance, evaluation, and AI/ML consumption.•    Experience with modern data and AI technologies, including Python and SQL, cloud and on-prem platforms, distributed processing, APIs, search, containers, machine learning, AI agents, MCP, and integration patterns.•    Practical experience with semantic modeling, ontologies, graph technologies, permission-aware retrieval, deterministic tools, and domain-specific evaluation, with the ability to align stakeholders across organizations.•    Strong communication and stakeholder management skills, with the ability to work with domain experts and explain complex data, semantic, and AI architecture topics clearly.•    A structured, collaborative, and result-oriented approach, combining architectural thinking with practical experimentation, hands-on implementation, and delivery in a complex technical environment.