Lead the development and optimization of retrieval systems that power context-aware large language models (LLMs)
Job Summary
Lead the development and optimization of retrieval systems that power context-aware large language models (LLMs).
Build robust Retrieval-Augmented Generation (RAG) pipelines to ensure AI agents and applications have access to the most relevant, timely, and high-quality information.
Work at the intersection of data engineering, machine learning, and knowledge management—enabling better reasoning, accuracy, and performance for enterprise-grade AI systems.
Matching Summary
Lead the development and optimization of retrieval systems that power context-aware large language models (LLMs).
Salary
Base: $172,500 -- $306,625 annually; Bonus/Equity: Not specified; Benefits: Not specified
Skills & Requirements
Must-have
Information Retrieval Engineer
RAG System Design
Vector Databases
Semantic Search Infrastructure
Embedding Generation
LLM Reasoning Support
Nice-to-have
Knowledge Graph Design
Enterprise-grade AI systems
Data Freshness Management
System Responsiveness Optimization
Key Requirements
4+ years in data engineering, ML infrastructure, or information retrieval
Experience building and deploying RAG pipelines
Strong ML and Python skills
Proficiency with embedding models
Familiarity with cloud platforms and MLOps tooling