AI Data Engineer

About the role

Kyndryl is hiring an AI Data Engineer in Madrid to build the high-performance data infrastructure that powers its autonomous systems. The engineer designs and scales real-time pipelines that turn unstructured IT logs and documentation into optimized vector embeddings, so that agentic AI can reason over complex enterprise environments.

Key responsibilities

• Design and scale Retrieval-Augmented Generation (RAG) pipelines, turning large volumes of unstructured IT logs and documentation into optimized vector embeddings.
• Keep vector databases (e.g. Pinecone, Milvus or Weaviate) healthy and performant, with sub-second retrieval speeds.
• Build knowledge graphs and semantic layers beyond simple ETL, giving agents the context they need.
• Set up automated data guardrails that detect noise, bias or PII before it reaches the model.
• Identify and fix data quality issues at the source, bridging raw data and deep technical AI work.
• Build, deploy and maintain CI/CD pipelines for the data infrastructure.

Required profile

• Expertise in data mining, data storage and ETL processes.
• Experience developing data pipelines and tooling such as Glue, Databricks, Synapse or Dataproc.
• Experience with relational and NoSQL databases, including PostgreSQL, DB2 and MongoDB.
• Strong problem-solving, analytical and critical thinking skills, and the ability to handle several projects at once.
• Ability to communicate with technical and non-technical colleagues and translate business needs into technical requirements.
• Preferred: data modelling experience, professional or cloud platform certification, understanding of social coding and IDEs such as GitHub and Visual Studio, and a degree in a scientific discipline such as Computer Science, Software Engineering or Information Technology.