High-Load Enterprise Ingestion & Inference Platform

Overview

Led the architecture and development of a scalable, multi-tenant RAG (Retrieval-Augmented Generation) platform designed to ingest and index massive volumes of heterogeneous enterprise data. The system enables automated monitoring of client-defined storage, transforming unstructured data into a queryable knowledge base powered by a variety of Large Language Models.

Achievements

Engineered a high-throughput data pipeline capable of processing diverse file formats with sub-second retrieval latency for inference. Successfully onboarded two enterprise clients, centralizing their fragmented internal knowledge management into a unified AI interface.

Responsibilities

• Directed two cross-functional teams (10+ engineers and architects) through the full SDLC, from initial discovery to production-grade deployment.
• Designed a modular ingestion architecture using Airflow to handle complex document partitioning, embedding generation, and metadata enrichment.
• Defined the technical roadmap and infrastructure standards, including the transition to a hybrid search approach (Elasticsearch + Vector embeddings).

Technologies Used