AI Data Engineer

- We design and build dependable, scalable data ingestion and integration pipelines for structured, semi-structured, unstructured, and multi-modal data sources, such as databases, files, documents, APIs, and events. - We ensure our data pipelines are AI-ready, governed, secure, and observable. - We apply data quality, validation, monitoring, and testing frameworks in production environments. - We follow enterprise governance, access management, and security standards, including role-based access and lineage requirements. - We monitor pipeline health, investigate failures, and improve performance, cost efficiency, and throughput. - We integrate AI services such as document understanding, content understanding, embeddings, search, and LLM APIs into production data workflows. - We build and maintain ETL and ELT pipelines using cloud-native services and distributed processing frameworks. - We develop production-grade Python services and REST APIs to expose data and AI capabilities. - We work with leadership to define outcomes and vision, then translate them into execution plans, architecture choices, and delivery milestones. - We create feature engineering pipelines that support ML and GenAI use cases, including retrieval-augmented generation (RAG). - We own the standards, best practices, and reusable frameworks that improve consistency and quality across teams and platforms. - We collaborate closely with cross-functional partners to align data input and output requirements clearly and consistently.