AI Data Engineer

Required Qualifications

Strong experience in Python application development.

Strong background in data engineering, data analytics, BI, or data application development.

Experience with cloud platforms and Kubernetes-based application/pipeline deployment.

Strong hands-on experience building production-grade data pipelines.

Strong understanding of data architecture, data processing, ETL/ELT, and data integration.

Experience working with databases, data warehouses, and/or modern data platforms.

Experience building data-facing applications or applications that interact directly with data and analytics platforms.

Hands-on experience developing LLM-powered applications.

Understanding of AI agent/harness engineering patterns and LLM application architecture.

Experience working with APIs, databases, data platforms, and enterprise data sources.

Cloud development experience with Kubernetes-based deployment.

Experience with agentic AI, LLM orchestration, RAG, or tool-using agents.

Experience with frameworks such as LangChain, LangGraph, LlamaIndex, or similar technologies.

Experience with vector databases and retrieval pipelines.

Experience integrating LLMs with enterprise data platforms.

Experience building dashboards, analytics applications, self-service data applications, or other data-centric user experiences.

Experience with AWS, Azure, or Google Cloud Platform.

Experience with Docker and Kubernetes.