AI Infrastructure Data Engineer || Remore role

AI Infrastructure Data Engineer Remore role Long Term Contract References must needed

Job Description:

Build the data backbone that powers AI

• pipelines, knowledge bases, ingestion, and retrieval infrastructure. Minneapolis (Hybrid)
• Intermediate / Senior
• 4-8 YOE
• Data pipelines required AI systems are only as good as the data feeding them.

This role owns the infrastructure that gets data from internal systems, document stores, APIs, and enterprise databases into vector indexes, knowledge bases, and structured stores that AI agents can reliably query. You'll build ingestion pipelines with freshness management, design chunking and embedding strategies, and ensure retrieval quality

• the hidden layer that determines whether agents give accurate answers or hallucinate.

This is not a traditional data warehousing role; it is data engineering specifically in service of AI systems.

WHAT YOU'LL BUILD

▸ Ingestion pipelines pulling from internal systems, APIs, document repositories, and enterprise databases into AI knowledge stores ▸ Vector indexing infrastructure

• embedding model selection, chunking strategies, metadata enrichment, hybrid index design ▸ Freshness and change detection
• incremental re-indexing, stale data detection, TTL management ▸

ETL / ELT

pipelines for structured data feeding AI decision and retrieval layers ▸ High-throughput event-driven ingestion for real

• time and batch processing at enterprise scale ▸ Data quality validation
• schema checks, completeness scoring, anomaly detection before indexing

REQUIRED EXPERIENCE

▸ 4+ years building production data pipelines

• orchestrated workflows, not one-off scripts ▸ Strong SQL
• query optimization, indexing, execution plans, large result sets ▸ Experience with vector databases or search infrastructure (OpenSearch, Pinecone, pgvector, Azure AI Search) ▸ Python data processing at scale
• Pandas, Polars, or equivalent ▸ Understands embedding models
• how to evaluate retrieval quality, why chunking strategy matters ▸ Cloud data stack
• AWS (Glue, S3, RDS) or Azure equivalent ▸ Can diagnose why a RAG system's retrieval is failing
• at the data layer

NICE TO HAVE

▸ Event streaming platforms

• event-driven pipeline design, high-throughput ingestion patterns ▸ Legacy enterprise RDBMS experience (DB2, Oracle, or equivalent) ▸ Document intelligence
• OCR pipelines, PDF/scanned document ingestion ▸ dbt, Airflow, or similar pipeline orchestration tooling ▸ Knowledge graph experience
• Neo4j, Amazon Neptune, RDF/SPARQL, ontology design ▸ Experience building knowledge bases specifically for LLM consumption
• not just generic warehousing ▸ Financial services data
• understanding of regulated data handling, PII, audit trails

TECH STACK

Python

• Pandas / Polars / PySpark

ETL / ELT

Pipelines Event Streaming Pipelines Vector Databases (pgvector

• Pinecone
• Weaviate) OpenSearch
• Hybrid Search Knowledge Graphs
• Graph Databases Neo4j
• Amazon Neptune RDF
• SPARQL
• Ontology Design Embedding Models
• Chunking Strategies Document Intelligence
• OCR Pipelines dbt
• Airflow
• Pipeline Orchestration Cloud Data Services (AWS / Azure) Relational Databases
• SQL Optimization Data Quality
• Schema Validation Docker
• Container Orchestration Enterprise API Integration
• ( Believe you can and you re halfway there. )
• Theodore Roosevelt Sayantan Das |

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