Backend Engineer (+ Equity) at LlamaIndex

Company Description

LlamaIndex is a Series A startup building the essential document infrastructure layer for the next generation of AI applications, supporting over 25 million monthly downloads on its open-source library.

About the role

Join a high-caliber team at LlamaIndex building core services like LlamaCloud and LlamaParse. You will own the backend lifecycle for tools powering AI agentic workflows at scale. This role focuses on designing robust APIs and scaling Kubernetes-based infrastructure to handle millions of documents for a massive global developer community.

Location: San Francisco, USA

Why this role is remarkable

• Scale massive impact with over 25 million monthly downloads on the open-source library, reaching the heart of the global AI developer ecosystem.
• Join a small, high-autonomy Series A team where you shape architectural decisions for critical AI infrastructure rather than just executing pre-defined tickets.
• Work at the cutting edge of AI agentic workflows, building the document processing layer that powers the world's most advanced LLM applications.

Responsibilities

• Design, develop, and maintain high-performance Python-based APIs (REST, GraphQL, or gRPC) that serve as the foundation for LlamaCloud and LlamaParse.
• Architect and manage Kubernetes-based deployments across AWS/GCP, ensuring the scalability and reliability of systems processing millions of documents.
• Collaborate with a lean engineering team to ship end-to-end features, from service architecture to CI/CD automation via GitHub Actions.

Qualifications

• Has 4+ years of production experience in Python, demonstrating engineering rigor from top-tier tech firms combined with the velocity of early-stage startups.
• Possesses deep expertise in Kubernetes, including deploying, scaling, and debugging complex containerized environments in production settings.
• Demonstrates a proven track record of shipping independently and thriving in fast-paced environments without the need for heavy management process or overhead.