Senior AI Data Engineer
Key Skills for This Role
Full Job Posting
Qualifications
- 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.
Responsibilities
- 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.
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