Job description

Join us and help shape the future of AI by defining the narrative around document understanding. ABOUT THE ROLE LlamaIndex makes enterprise data useful for AI applications. You will own engineering execution, technical direction, hiring, and team development. You will work closely with company leadership and product teams to decide what we build, how we build it, and how we deliver it reliably to customers. This is a hands-on role. You should be comfortable reviewing code and architecture, debugging production issues, and writing code for critical systems. We are looking for someone who has shipped and operated B2B API products and understands both model serving and model training—from experimentation and evaluation to deployment and production operations. RESPONSIBILITIES - Lead and develop our 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 ML infrastructure, balancing immediate customer needs with long-term maintainability. - Work with ML 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. REQUIRED QUALIFICATIONS - 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: you can read and write production code, review architecture, and diagnose complex technical problems. - Practical familiarity with model serving and model training, including training or fine-tuning workflows, evaluation, deployment, and production operations. - Understanding of the tradeoffs between 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. PREFERRED QUALIFICATIONS - Experience scaling engineering at a Series A–C startup. - Experience with document processing, OCR, extraction, retrieval, indexing, or other systems that work with enterprise data. - Experience with LLMs 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. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. LlamaIndex does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees, or any other organization location. LlamaIndex is not responsible for any fees related to unsolicited resumes.

Requirements

Must-haves

Join us and help shape the future of AI by defining the narrative around document understanding.
LlamaIndex makes enterprise data useful for AI applications.
Lead and develop our 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 ML infrastructure, balancing immediate customer needs with long-term maintainability.
Work with ML 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.
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: you can read and write production code, review architecture, and diagnose complex technical problems.
Practical familiarity with model serving and model training, including training or fine-tuning workflows, evaluation, deployment, and production operations.
Understanding of the tradeoffs between 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.

Nice-to-haves

Experience scaling engineering at a Series A–C startup.
Experience with document processing, OCR, extraction, retrieval, indexing, or other systems that work with enterprise data.
Experience with LLMs 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.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapiml

Platform: ashby
Effort: quick