End-to-End AI Data Infrastructure Build

I will design and build a complete AI data infrastructure for your organisation — covering automated data ingestion pipelines, data transformation and quality validation, feature store setup for ML model serving, vector database for retrieval-augmented applications, data monitoring and alerting, and a documentation layer giving your engineering team full visibility into your data lineage and pipeline health. This is a foundational engineering engagement that transforms your data landscape from a collection of disconnected sources into a coherent, production-grade AI data platform your models can rely on.

The build covers source data connectors (API, database, file, or streaming), transformation pipelines in Python with orchestration via Airflow or Prefect, data quality checks with Great Expectations, feature store setup (Feast or custom), vector store build for retrieval applications, pipeline monitoring dashboards, alerting for data drift and pipeline failures, and full infrastructure-as-code documentation for reproducible deployment. All components are containerised with Docker and deployed to your target cloud environment.

This service is designed for ML engineering teams, data-driven startups, and scale-up businesses that need a professionally engineered AI data platform — built to production standards with monitoring, documentation, and maintainability — rather than a collection of scripts held together by institutional knowledge and manual processes.