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LlamaIndex

Connects data to large language models

Technical Lead Manager - Platform Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA$200k - $300k /yrHybrid

About the Job

Requirements

  • Experience leading multiple engineering teams in B2B SaaS, at a scope comparable to a 20–30-person organization, with responsibility for delivering and operating customer-facing products.
  • Strong, current hands-on engineering skills, including reading and writing production code, reviewing architecture, and diagnosing complex technical problems.
  • Practical familiarity with model serving and model training, including training or fine-tuning workflows, evaluation, deployment, and production operations.
  • Understanding of tradeoffs among model quality, latency, throughput, GPU resources, reliability, and cost.
  • Strong background in backend systems, APIs, distributed systems, and cloud infrastructure.
  • A track record of hiring and developing engineers, setting clear expectations, and giving constructive feedback.
  • Good product judgment, including knowing when to move quickly and when to invest in correctness, reliability, and security.
  • Clear written and verbal communication with technical teams, customers, and company leadership.

Responsibilities

  • Lead and develop the engineering team, setting clear priorities, ownership, and accountability.
  • Partner with company leadership to translate customer needs into a focused roadmap and deliver software predictably.
  • Stay directly involved in architecture, code reviews, production debugging, and implementation of critical changes.
  • Set technical direction across application services, APIs, data pipelines, and machine-learning infrastructure, balancing immediate customer needs with long-term maintainability.
  • Work with machine-learning engineers and researchers on model training, fine-tuning, evaluation, and deployment, ensuring model improvements translate into better production outcomes.
  • Guide model-serving decisions across latency, throughput, GPU utilization, capacity, reliability, and inference cost.
  • Improve engineering practices for testing, observability, security, incident response, and releases.
  • Hire strong engineers, coach technical leaders and managers, and build a culture of direct communication, customer focus, and responsibility for results.

Desired Qualifications

  • Experience scaling engineering at a Series A–C startup.
  • Experience with document processing, optical character recognition, extraction, retrieval, indexing, or other systems that work with enterprise data.
  • Experience with large language models or multimodal models, evaluation datasets, and model quality monitoring.
  • Experience with GPU infrastructure, distributed training, inference optimization, or model-serving frameworks.
  • Experience building developer tools or API-first products.
  • Experience with enterprise requirements such as multi-tenancy, access controls, auditability, and security reviews.
  • Experience developing engineering managers while staying closely involved in technical execution.

About the Company

LlamaIndex

Connects data to large language models

LlamaIndex.ai builds a data framework that helps businesses connect their own data sources to large language models (LLMs). It supports integrating structured data (like Excel and SQL), semi-structured data from APIs (such as Slack, Salesforce, and Notion), and unstructured data (web pages and images) so LLMs can understand and draw insights from a company’s data. The product works by linking diverse data sources to LLMs, enabling queries and data-driven answers across a company’s systems. Compared with competitors, LlamaIndex.ai emphasizes flexible connectors and a unified framework to access multiple data types through LLMs, making it easier for businesses to leverage AI on their existing data without heavy custom engineering. The company’s goal is to help organizations make data-driven decisions by simplifying access to their data through AI-powered language models, typically via a subscription-based B2B model.

Company size
51-200
Company stage
Series A
Total funding
$27.5M
Headquarters
San Francisco, CA
Founded
2023

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