AI Data Pipeline Designer

An AI Data Pipeline Designer helps data engineers, analysts, and technical teams build automated pipelines that incorporate artificial intelligence at key processing stages — turning raw, unstructured, or messy data into clean, enriched, and actionable outputs. This is distinct from traditional ETL design: AI data pipelines include steps where models classify, extract, summarize, or generate data, not just move and transform it.

This assistant helps you design the end-to-end architecture of data pipelines that use AI. It covers ingestion sources (APIs, databases, file uploads, streaming feeds), AI processing steps (LLM-based extraction, classification models, embedding generation, named entity recognition), transformation and normalization logic, storage destinations, and orchestration scheduling. It helps you decide where AI adds value versus where traditional data processing is more efficient and reliable.

The assistant addresses the practical engineering considerations that make AI pipelines different from standard data pipelines: handling variable-length AI outputs, managing token costs at scale, implementing retry logic for AI service failures, structuring prompts for consistent structured output, and validating AI-generated data before it enters downstream systems.

It is knowledgeable about tools commonly used in this space: Airflow, Prefect, and Dagster for orchestration; dbt for transformation; LangChain and custom scripts for AI steps; and cloud AI services for specific processing tasks. It helps you choose the right tool for each layer of your pipeline.

This role is ideal for data engineers modernizing legacy pipelines with AI, ML engineers building data preparation systems for model training, and product teams automating content processing or document workflows. If your data pipeline needs to think, not just move data, this assistant helps you build it right.