AI Data Engineer - Real-Time RAG & Semantic Pipelines

Kyndryl Inc.; Madrid; Hybrid; EUR 60,000 - 90,000; Full time

Job summary

Kyndryl is seeking an AI Data Engineer to design high-performance data infrastructure powering autonomous systems. You will build real-time pipelines, optimize vector embeddings, and ensure safe data handling for AI models in enterprise environments.

The role emphasizes data mining, ETL, pipelines, and scalable cloud tooling, with hybrid work options and collaboration across technical and non-technical teams. You will influence data quality and production readiness from inception to deployment.

Qualifications

• Expertise in data mining, data storage and ETL processes.
• Experience in data pipelines development and tooling (Glue, Databricks, Synapse or Dataproc).
• Experience with relational and NoSQL databases (PostgreSQL, DB2, MongoDB).
• Excellent problem-solving, analytical and critical thinking skills.
• Ability to manage multiple projects with high attention to detail.
• Ability to translate business needs into technical requirements.

Responsibilities

• Architect for RAG pipelines to transform large IT logs into optimized embeddings.
• Scale vector infrastructure for sub-second retrieval in agentic loops.
• Engineer semantic layers and knowledge graphs for contextual reasoning.
• Automate data guardrails to detect noise, bias or PII before model consumption.
• Build, deploy and maintain CI/CD pipelines for data infrastructure.

Skills

Data mining

ETL processes

Data pipelines

Analytical thinking

Cross-functional

Education

Degree in Computer Science

Tools

Glue

Databricks

Synapse

Dataproc

PostgreSQL

DB2

MongoDB

Job description

Kyndryl is seeking an AI Data Engineer to design high-performance data infrastructure powering autonomous systems. You will build real-time pipelines, optimize vector embeddings, and ensure safe data handling for AI models in enterprise environments.

The role emphasizes data mining, ETL, pipelines, and scalable cloud tooling, with hybrid work options and collaboration across technical and non-technical teams. You will influence data quality and production readiness from inception to deployment.