AI Data Engineer: Real-Time Vector & RAG Pipelines

Kyndryl Inc.; Dallas, Northern (TX, KY); Hybrid; USD 63,000 - 114,000; Full time

Benefits offered by this job

Discretionary annual bonus

Comprehensive benefits package

Learning and certification programs

Job summary

Kyndryl Inc. seeks an AI Data Engineer to design high-performance data infrastructure powering autonomous systems. You will build real-time pipelines for RAG, manage vector databases for fast retrieval, and create semantic layers and guardrails to ensure data quality and privacy.

Responsibilities include production-ready data pipelines, knowledge graphs, and scalable data transformations, with a focus on reliability and governance across complex enterprise environments.

Qualifications

• Expertise in data mining, storage and ETL processes.
• Experience building data pipelines and tooling (Glue/Databricks/Synapse/Dataproc).
• Working with relational and NoSQL databases (PostgreSQL, DB2, MongoDB).
• Strong problem solving, analytical and communications skills.
• Ability to manage multiple projects with attention to detail.

Responsibilities

• Architect real-time data infrastructure for agentic AI.
• Design and scale RAG pipelines with vector embeddings.
• Build semantic layers and knowledge graphs for context.
• Develop automated data guardrails to protect data quality and privacy.
• Create CI/CD pipelines for data infrastructure production.

Skills

Data mining

Data storage

ETL

Data pipelines

Problem solving

Analytical thinking

Communication

Project management

Education

Open Certified Technical Specialist – Data Engineering

AWS Certified Data Analytics – Specialty

Google Cloud Professional Data Engineer

Azure Data Engineer Associate

Tools

Glue

Databricks

Synapse

Dataproc

PostgreSQL

DB2

MongoDB

Pinecone

Milvus

Weaviate

GitHub

Visual Studio

Job description

Kyndryl Inc. seeks an AI Data Engineer to design high-performance data infrastructure powering autonomous systems. You will build real-time pipelines for RAG, manage vector databases for fast retrieval, and create semantic layers and guardrails to ensure data quality and privacy.

Responsibilities include production-ready data pipelines, knowledge graphs, and scalable data transformations, with a focus on reliability and governance across complex enterprise environments.