Senior AI Data Engineer

The AI & Data Engineering team builds the engineering foundation for Technology Standards’ transformation into an AI-first organization. Our scope spans Agentic AI and AgentOps, data engineering, common software platforms, and compute and simulation capabilities.

Our Data Engineering mission is to ingest and process external standardization data, including 3GPP, O-RAN, JVET, MPEG, Wi-Fi and ITU-R, together with internal high-value datasets, into reliable, linked and AI-ready data assets.

As a Senior AI Data Engineer, you will take end-to-end ownership of data engineering solutions supporting Q365, Technology Standards Research & Innovation, and Patenting. You will work hands-on across architecture, data pipelines, cloud platforms, metadata, quality and production operations, collaborating closely with AI engineers, researchers, standards experts and patenting stakeholders.

The role combines strong software and data-engineering capabilities with the ability to translate research and business needs into scalable, reliable and production-ready solutions.
 

You have:

• Strong hands-on experience in production-grade data and software engineering.
• Strong Python skills and experience designing automated data pipelines.
• Experience with distributed data processing, data modelling, metadata and data quality.
• Experience with PostgreSQL, Elasticsearch or comparable data and search technologies.
• Cloud-native engineering, Kubernetes and container experience.
• Knowledge of CI/CD, monitoring and production operations.
• Ability to translate complex or ambiguous requirements into scalable technical solutions.
• Strong ownership, collaboration and communication skills, with the ability to guide other engineers while remaining hands-on.

 

It would be nice if you also had:

• Experience with Microsoft Azure and Microsoft Fabric.
• Experience with Spark, BigQuery, OLAP or similar data and analytics technologies.
• Experience with LLM-assisted data processing, embeddings, vector search or semantic retrieval.
• Knowledge of knowledge graphs, linked data or semantic-data architectures.
• Experience with Power BI, Apache Superset or similar visualization tools.
• Familiarity with 3GPP, O-RAN, Wi-Fi, MPEG, JVET or ITU-R data.
• Experience with research, standardization, intellectual property or patenting-related data and use cases.

 

• Own data solutions from requirements and architecture through deployment, operation and continuous improvement.
• Build scalable ingestion, transformation and processing pipelines for standards and strategic datasets.
• Transform complex information into structured, reliable and AI-ready data assets.
• Develop AI/LLM-assisted data processing, semantic ingestion and retrieval capabilities.
• Strengthen metadata, data quality, lineage, freshness, observability and reliability.
• Develop and operate solutions using Python, Kubernetes, Spark, PostgreSQL, Elasticsearch and Microsoft Fabric.
• Enable AI, LLM and agentic applications through high-quality data and knowledge services.
• Provide technical guidance and knowledge sharing while remaining hands-on in solution delivery.